{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Newly Predicted Cancer Genes\n",
    "This notebook will generate a consensus list of new predictions (genes that were not previously listed as cancer genes but that we  are predicting as new candidates). It will further prove that the predictions are meaningful by:\n",
    "\n",
    "1. Showing that our new predictions interact with lots of known cancer genes\n",
    "2. Showing that our new predictions tend to be essential in tumor cell lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data science classics\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys, operator\n",
    "import scipy\n",
    "\n",
    "# utility functions\n",
    "sys.path.append(os.path.abspath('../EMOGI'))\n",
    "import postprocessing\n",
    "import gcnIO\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "#plt.rc('text', usetex=True)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relevant Paths\n",
    "\n",
    "* `all_models_dir`: The directory in which models for all relevant PPI networks reside. The directory should have the network names as folders and each of the folders then must contain the results from multi-omics-trainings.\n",
    "* `reference_model_dir`: The directory where the most relevant model resides. Can be one of the directories inside the `all_models_dir` but not a multi-omics training.\n",
    "* `achilles_data_path`: Path to the achilles CRSIPR gene effect csv file that can be downloaded [here](https://depmap.org/portal/download/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_models_dir = '../data/GCN/training/final_TCGA_all_networks/'\n",
    "#reference_model_dir = '../data/GCN/training/final_TCGA_all_networks/CPDB/multiomics/'\n",
    "all_models_dir = '../data/GCN/training/Rev1_CNA_separated_all_networks/'\n",
    "reference_model_dir = '../data/GCN/training/Rev1_CNA_separated_all_networks/CPDB/'\n",
    "achilles_data_path = '../data/pancancer/Achilles/Achilles_gene_effect.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters\n",
    "\n",
    "* `top_n`: The number of top predictions per model to consider for computation of newly predicted cancer genes (consensus calculation)\n",
    "* `all_omics`: Whether to use all omics (Mutation, DNA Methylation, Expression and combinations of two types of data) or only the multi-omics models for calculation of newly predicted cancer genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_n = 100\n",
    "all_omics = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Compute Consenus Predictions from different Multi-Omics Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPDB\n",
      "PCNet\n",
      "STRINGdb\n",
      "IRefIndex\n",
      "Multinet\n",
      "IRefIndex_2015\n",
      "165 different non-cancer genes in top 100 predictions across all 6 PPI networks!\n"
     ]
    }
   ],
   "source": [
    "consensus_predicted = {}\n",
    "checked_models = 0\n",
    "for network in os.listdir(all_models_dir):\n",
    "    network_path = os.path.join(all_models_dir, network)\n",
    "    if os.path.isdir(network_path) and not network == 'CPDB_notsogood' and os.path.isfile(os.path.join(network_path, 'hyper_params.txt')):\n",
    "        print (network)\n",
    "        model_path = network_path\n",
    "        predictions_topn = postprocessing.load_predictions(model_path).head(top_n)\n",
    "        non_cancer_hits = predictions_topn[~predictions_topn.label]\n",
    "        for gene in non_cancer_hits.Name:\n",
    "            if not gene in consensus_predicted:\n",
    "                consensus_predicted[gene] = 1\n",
    "            else:\n",
    "                consensus_predicted[gene] += 1\n",
    "        checked_models += 1\n",
    "\n",
    "#consensus_predicted = {k: consensus_predicted[k] for k in consensus_predicted if consensus_predicted[k] > 1}\n",
    "print (\"{} different non-cancer genes in top {} predictions across all 6 PPI networks!\".format(len(consensus_predicted), top_n))\n",
    "consensus_sorted = sorted(consensus_predicted.items(), key=operator.itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "consensus_predicted = {}\n",
    "checked_models = 0\n",
    "for network in os.listdir(all_models_dir):\n",
    "    print (network)\n",
    "    network_path = os.path.join(all_models_dir, network)\n",
    "    if all_omics:\n",
    "        if os.path.isdir(network_path) and not network.startswith('.'):\n",
    "            for omics in os.listdir(network_path):\n",
    "                model_path = os.path.join(network_path, omics)\n",
    "                if os.path.isdir(model_path) and not omics.startswith('.'):\n",
    "                    predictions_topn = postprocessing.load_predictions(model_path).head(top_n)\n",
    "                    non_cancer_hits = predictions_topn[~predictions_topn.label]\n",
    "                    for gene in non_cancer_hits.Name:\n",
    "                        if not gene in consensus_predicted:\n",
    "                            consensus_predicted[gene] = 1\n",
    "                        else:\n",
    "                            consensus_predicted[gene] += 1\n",
    "                    checked_models += 1\n",
    "    else:\n",
    "        if os.path.isdir(network_path) and os.path.isdir(os.path.join(network_path, 'multiomics')):\n",
    "            model_path = os.path.join(network_path, 'multiomics')\n",
    "            predictions_topn = postprocessing.load_predictions(model_path).head(top_n)\n",
    "            non_cancer_hits = predictions_topn[~predictions_topn.label]\n",
    "            for gene in non_cancer_hits.Name:\n",
    "                if not gene in consensus_predicted:\n",
    "                    consensus_predicted[gene] = 1\n",
    "                else:\n",
    "                    consensus_predicted[gene] += 1\n",
    "            checked_models += 1\n",
    "\n",
    "#consensus_predicted = {k: consensus_predicted[k] for k in consensus_predicted if consensus_predicted[k] > 1}\n",
    "print (\"{} different non-cancer genes in top {} predictions across all 7 omics data types!\".format(len(consensus_predicted), top_n))\n",
    "consensus_sorted = sorted(consensus_predicted.items(), key=operator.itemgetter(1), reverse=True)\n",
    "\"\"\"\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_plot = consensus_sorted[:80]\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h')\n",
    "plt.title('Consensus Top {} Genes Across All Omics & Networks ({} Models)'.format(top_n, checked_models), size=20)\n",
    "plt.xlabel('# of Models where Gene was in Top {} Genes'.format(top_n), size=20)\n",
    "#plt.gca().tick_params(axis='y', labelsize=10)\n",
    "fig.savefig(os.path.join(all_models_dir, 'consensus_genes_{}omics.svg'.format('all_' if all_omics else 'multi')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_models</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HDAC2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSPA8</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FYN</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEK1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIST2H2BE</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTK2B</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USF1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_models\n",
       "Name               \n",
       "HDAC2             6\n",
       "HSPA8             3\n",
       "FYN               3\n",
       "CHEK1             2\n",
       "HIST2H2BE         1\n",
       "PTK2B             1\n",
       "USF1              1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npcg_df = pd.DataFrame(consensus_sorted, columns=['Name', 'N_models']).set_index('Name')\n",
    "cluster_12 = pd.read_csv(os.path.join(reference_model_dir, 'bicluster_9.tsv'), sep='\\t')\n",
    "npcg_df[npcg_df.index.isin(cluster_12.Name)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Compute Top Neighbors of Newly Predicted Cancer Genes\n",
    "After deriving a list of newly predicted cancer genes (NPCGs), I want to see if they are in close proximity to known cancer genes or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read predictions from 10 CV runs\n",
      "Data comes from ../../../../pancancer/rev1_container_all_networks_CNA_separated/CPDB_multiomics_cnaseparate_samesplit.h5\n"
     ]
    }
   ],
   "source": [
    "def get_all_neighbors(gene, A, node_names):\n",
    "    nodes = [x[1] for x in node_names]\n",
    "    if gene in nodes:\n",
    "        idx = nodes.index(gene)\n",
    "        interaction_partners_idx = np.where(A[idx, :] == 1)[0]\n",
    "        interaction_partners = node_names[interaction_partners_idx]\n",
    "        return interaction_partners\n",
    "    else:\n",
    "        return [(None, None)]\n",
    "\n",
    "# load the model of choice\n",
    "args, data_file = gcnIO.load_hyper_params(reference_model_dir)\n",
    "data = gcnIO.load_hdf_data(os.path.join(reference_model_dir, data_file))\n",
    "adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "_ = postprocessing.compute_ensemble_predictions(reference_model_dir, comprehensive=True) # make sure predictions contain DBs\n",
    "predictions = postprocessing.load_predictions(reference_model_dir)\n",
    "print (\"Data comes from {}\".format(data_file))\n",
    "\n",
    "consensus_neighbors = {}\n",
    "for gene, num_predicted in consensus_sorted:\n",
    "    partners = get_all_neighbors(gene, adj, node_names)\n",
    "    for partner_id, partner_name in partners:\n",
    "        if partner_name in consensus_neighbors:\n",
    "            consensus_neighbors[partner_name] += 1\n",
    "        else:\n",
    "            consensus_neighbors[partner_name] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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N0n9lq5DucHmddEtxdzgY2JU0V0Q1XyP9l3uq0qyzd5Oaj5cGPkt6/9bNdaoq9yH/kPSH6g5J/yC9t+uRgqJbSK0cH3bD9UC6BfxSSZeSZjVdk/RZv8mcc4nsQxpod7TShHEtpC/uXXN9vtnBuwL+CCwl6d+kW85nAGuRPuvnSPPhtCoi/i5pe2A34GFJ/yS1bO0AfBK4ICLO7UCdOuMM0h+iz9TYfyCpheBHOcC+mdTFtBspYPlRRDxb5bhLSX9If0r6ffpTtfEDEXGa0uRdPwCellS5E2sY6T3YkPTHfZ/OXV63+xGpi/U3wF55oPVrpBl5VyGNPRkFVN6T84DdSWMq/ifpMmZ/v9xLCqLbFBFvKK3rcxFws6SrgYdId+p8lvSzXAyIbgT2kHQFqZVjJnBrG99l3f07UvaVXPadpO7p10nfL9vn8qt9N/dt0QfuWfbWdzaqzGNS2LcHKQh5C3if1Fx8MIV5BErlTCAN/jqZNLhsOmmm1h9TmLuhjfoMIv0HdD0pKHmf1E3zFGkU+xdbOXYYacKs50h/4F4h/Te9dHvOXSjnjPyebFZjf2VugaA0j0nevxApULqPdIvoe6Qv2KtId28sUMg7mtI8JoV9W5HucnqX9F/WZaS1SSrzZwwp5B1BlTkVCvvHU5qDonhu0uROd5IC0Cmk+UU+VaOspUiDMSvv8xukScHWrpK35vXl/buR/ug8md+raaSA9Ahg0Q58ZnOR/ii35Pfr3fz+/xCYq5XPeI6f+1bOUbmWw2vsr8wXE5TmMcn7hwBH5Wudnt/n64HN2zjv3wrlrtVG3m3zz8fr+bN5lRSkHw6sXMpbc46OGmVH+WeolbxjKc0hUiXP3KQA5Q5mz+XyPCkY+CkwvEr+XwPP5LwT8s/JPNWupbU6kMYwnUX6nppBCopuAcaU8i1GGmj8GqmlNoCxbf3O0b2/Ix+7NlLgdlz+WZ9YeC8uAtZr7+fZlzblCzPrVspTTUfEiPrWpLHlZu9ngLkjYom28puZ9XUeY2LWD0gaUhwvkdNEak1ali70UZuZ9SUeY2LWP6wDXCDpOlIz7YI5bU3SpEtj61UxM7Pu5MDErH94nDRWYH3SQl4DSWNu/gj8LtKdDWZm/Z7HmJiZmVmf4TEmZmZm1me4K6cOFllkkRgxYkS9q2FmZtZr7rvvvjciYtG28jkwqYMRI0bQ0lJt4UgzM7PGJOm59uRzYFIHsya+ycQ/V1sR3MzMrL4W/f7X63r+hhhjIinasW2ct2LaZEm3S9q0UNaIGsefXzrnYZL+K2mapLcktUjavfev3szMrHE0SovJuoXn85HWJjmcNOV3xSNAZdGqPUmzZQ4nrQB6jaS1I+LBQv6fk9Y3qCguKw5pLYUzcrkfkNZoOF/SBxFxUVcuxszMrFk1RGASEZWlqyvLwAM8XUzP+ypPH4qI/+W0W0gTVH2Xjy/s9nj5+NI59y0lXSdpNWBv0hoFZmZm1kEN0ZXTFRHxNmlFxhHdUNwk0sJSZmZm1glNH5jkRdCWIa26WXS6pA8kvSLpOEnz1Th+YF7HZE9gc+AvPVxlMzOzhtUQXTmdMEDSQGAYcDCwBLMXQZsOnAxcR1pyfWPgAGAFYPtiIZLWIS0NDzAL+FFE/LOH625mZtawmjUwebDw/B3ggIi4EiAiXgF+VNg/XtJrwCmS1oiI/xT2/RdYGxgCbAOcJGlaRJxXPqGkMcAYgKWHDe/GSzEzM2sczRqY7AE8DUwGnouIWW3kvwg4BVgL+CgwiYh3gMpMaTdIGgwcBcwRmETEOGAcwJrLLe8FiszMzKpo1jEmD0dES0Q83Y6gBCBKj7XcDyyTu4nMzMysg5o1MOmoXfLjfW3kWx94sZ3BjpmZmZX4P/sSSWOBhUiTq00DNgT2By6JiIdynuWA04DzSV1CCwI7krqIvt/7tTYzM2sMDkzm9Bhp1tfvkGaRfR44GjiikGcK8DLwS9IdPVNIM8BuExH/6sW6mpmZNZSGC0zyhGmqsW98rX2FPOeTWkJayzMV2KuTVTQzM7MaPMbEzMzM+oyGazHpDwYuOqzuy0qbmZn1RW4xMTMzsz7DgYmZmZn1Ge7KqYNZE1/n9b/8sd7VMDOzBrPYPj+udxW6rM+2mEgaKykkXVtl30WSxuct2tjG5mOKae9JelTSAeVZWiWdIamllDafpJskvSnpczltd0mX5NWHQ9Lonns3zMzMmkN/aDHZXNLaEXFvlX0/ABYuvD4deAb4bSHtxcLzY0nr3swHbAv8HhgEHF7r5JLmIa08vBawWUQ8kHftAowAriTNeWJmZmZd1NcDkzeBl4CDgR3KOyPikeJrSe8AEyPirhrlTSjsu1nSasDe1AhMJA0C/gFsAGxRCo52j4gPJS2IAxMzM7Nu0We7crIgzbj6VUmf6YHy/wMsU22HpAHAucBXgG0j4t8fq1jEhz1QHzMzs6bW1wMTgAuBJ0mtJt1tWeDZKulzAWcAXwV2yDPGmpmZWQ/r84FJbpk4EthV0qe6WNxckgZKWkjSKGAn4DdV8n0O+Drwi4i4rovnNDMzs3bq84FJdg5pMb2DuljOicBM0qrBfwdOzmvjlD1DWjV4f0lVu3o6StIYSS2SWia9/XZ3FGlmZtZw+kVgEhGzgD8AX5e0XBeKOhpYG9iMdDfNvpK2rpJvMrA5aXDwtZKGdeGcAETEuIgYGREjhy+4YFeLMzMza0j9IjDJTgNeBw7oQhnPR0RLRNxI6sZ5HDha0hwrDkfEM8BWwFLAVZLm78J5zczMrB36TWASEdOBY4BvAUt0Q3kzgUOAVYHtauR5kHSb8ueAC8uTsZmZmVn36jeBSfZX4C1gvW4q72LgMWD/Whki4mZgT2BL4NRK64qkVSXtwuz5VUZK2kXSRt1UNzMzs6bTrwKTiHgXOL4by6vc8bOBpHVayXcx8EPSZGx/yMm7kW5lPju//mF+fVh31c/MzKzZKCLqXYems+Zyy8Z1B/283tUwM7MG05cX8ZN0X0SMbCtfv2oxMTMzs8bmwZx1MHDRxfp0VGtmZlYvbjExMzOzPsOBiZmZmfUZ7sqpg5mvv8DLJ/+s3tUwM7MGsOQPj6t3FbqVW0zMzMysz+j1wETSzpJukjRF0nRJT0g6TtI4SdHGNj6XMT6/nmNRP0lvSBpbeD025722St6LKmXm1xuXzjdT0jOS/iBpgdKxu0u6RNIrOe/o7nuXzMzMmlOvduVIOhb4KXA6aaK0aaQp4fcB3gTWLWT/f8CXgR0LadNKRe4r6cQ88VpbNpe0dkTc2468e5JWGB4EfB44AhgCjCnk2QUYQVoM8DvtKNPMzMza0GuBiaTtgJ8B346I0wq7bpE0Dtg8Iu4q5N8FmF5MK7mTFDSMAU5o4/RvAi8BBzN7CvnWPBQR/8vPb5O0FDCajwcmu0fEh5IWxIGJmZlZt+jNrpx9gftLQQkAEfFBRFzdwfJeJrW8/FzSPG3kDVKrx1clfaaD54G0Ps+gjxWYprM3MzOzbtQrgYmkQaSF967p5qKPAhYHvtmOvBcCT5JaTdoyQNJASfNJWh/4HnBp56tpZmZm7dFbLSbDgXmA57uz0IiYAJwL/EJSq91ShQX7dpX0qTaKfhCYCbwL3A48C/ykq/U1MzOz1vX2XTk9sWLgkcBypAGrbTmHFBzNcTdPyR7A2sAXgVHAIsA/JXX6/ZI0RlKLpJZJb7/X2WLMzMwaWm8FJpOA6cCy3V1wRDwOXAQc2FbgEBGzgD8AX5e0XCtZH46Iloi4JyLOJwU9Xwa27kI9x0XEyIgYOXzB+TpbjJmZWUPrlcAkImYC/wa26KFTHAF8mnQLb1tOA14HDuhA+Y/mx1U6WC8zMzPrgN7syjkBGCnpG+UdkuaStGVnC46Ih4ArgF8CaiPvdOAY4FvAEu08xer58YXO1tHMzMza1mvzmETEFZKOA07Nd7pcBrwNrEyaYG0CXbtr5wjg7nbm/SspiFkPuKXK/s/m+UnmApYHDiGNTfnolmZJq5Imh5s3J42U9DYwMSKqlWlmZmZt6NWZXyNiP0l3AD8C/g7MRwpILie1YnSl7HskXQ98pR1535V0PCmYqebcSlbSfCm3Ab+KiKmFPLsBhxZe/zBvtwAbd6z2ZmZmBqCInrhRxlqzxrKLx9UHtOcmIjMzs9b1l9WFJd0XESPbyterLSaWDFpsmX7zg2RmZtaben11YTMzM7NaHJiYmZlZn+GunDp4//WneOzk7etdDTMza6eVf3hZvavQNNxiYmZmZn1GQwQmksZKihrb13Oe5SSdLel5Se9LekHSZZI2bKWcVyVdKemzVc55iKQbJE3LeUf04iWbmZk1pEbqypkKVJs99ilJQ4G7gFdIC/i9DIwAvgqsC9xao5wRwG+A6yWtEhFvFvJ9D3gKuDmXY2ZmZl3USIHJrIi4q9oOSd8FFgfWiIjXC7tOl1Sewr5Yzl2SJgB3koKVvxfyLRsRH0raFgcmZmZm3aIhunLaYQgwA3izvCPanmHuP/lxmdJxH3ZLzczMzOwjDRWYSBpY3vKu+4F5gLMlrSWpI9e9bH58tlsra2ZmZnNopMBkODCzvEkaERE3AscDuwMtwBRJF0varFpBhcBmBeAk4EHSooNmZmbWgxppjMlUoFqg8TJARPxM0inADsCGpDEjO0r6QUT8pZC/EuBUTALWjojpXamcpDHAGIAlh87XlaLMzMwaViO1mMyKiJYq24xKhoh4KiKOiYivAsuRWkJ+VxoAOxVYG1iHdOfN3MDfO9j9M4eIGBcRIyNi5NAF5+5KUWZmZg2rkQKTDomIN4DTgaHAYoVdlQDn7ogYB/yQFKTsWodqmpmZNZWmCEwkLVpj10rAdFIrSS3nAA8DB3R3vczMzOzjGmmMyUBJ61RJfwEYJWlP4CzS7b+DSONRfgD8OSLer1VoRISk3wHnSto0D6RF0kbAosBaOetWkiYCj0TEI912VWZmZk2kkQKTwaSJ0MoOAS4BPgl8lzQfyQfA08D/A/6vHWVfAIwFfgHcmNMOAzYq5DmlkD62QzU3MzMzANT2/GLW3VZfdkhcdMBGbWc0M7M+wasLd52k+yJiZFv5GqnFpN+Yd7EV/UNuZmZWRVMMfjUzM7P+wYGJmZmZ9RnuyqmDdyY+xZ3jtq13NczMrBXrjrmy3lVoSm4xMTMzsz6j3wcmksZKihrb1wv5Fpd0nKQnJL0v6W1J9+XjFynkG12jrKcKecYX0mdJmiDpr61M5GZmZmbt0ChdOVNJi/KVPQUgaWXgJuBd4I/Af0lr4KxHmmRtBWCv0rFfBt4rvC5PwnYz8EvSe/h54PBcTtUVi83MzKxtjRKYzIqIu1rZ/3fgDWCDiJhWSL9W0rHANlWOuTci3m6lzDcL57xd0vzAkZKWjIiXO1R7MzMzAxonMKkpTx3/OWCbUlACQE47rxtO9Z/8uAzgwMTMzKwTGiYwkTTHtUTELGBDYBapK6cjBpTK/CBanyZ3WeBD4LkOnsfMzMyyfj/4NRsOzCxvkkYASwJvlBfqkzRA0sC8DahS5pRSed8u7Vc+dh5J6wEHAeMi4tVuvC4zM7Om0igtJlOpPui00qVSraVjKrBAfj4JWKS0f0M+Pvh1Qmn/TqSApeJu4Me1KihpDDAGYPFh89XKZmZm1tQaJTCZFREt1XZIehlYVNI8ETG9sOtLwABSsLBTlUMfaGPw603AAcA8wHb5+eH5cQ4RMQ4YB7DKckO8cqKZmVkVjRKYtOZW0nVuAlxTSYyIBwAkdXYK1smFYOjfeQ6Tn0o6KSJe6EqFzczMmlWjjDFpza3AA6RbeRfqwfMcmh/37cFzmJmZNbRGaTEZKGmdKukvRMRLkr5GmhDtfkl/Ik2wNgBYCdgdaK3Lpl0i4kVJZwLflfSbiJjS1TLNzMyaTaO0mAwG7qyyfRMgIh4jzWVyBfD/gKuBfwLfBf4BjOymevwemBf4fjeVZ2Zm1lTU+tQc1hNWWW5InHbwBvWuhpmZtcKrC3cvSfdFRJsNAY3SldOvLLDoiv6BNzMzq6JRunLMzMysATgwMTMzsz7DgYmZmZn1GR5jUgfT3niSa0/dut7VMDOzki2+/a96V6Hp9YsWE0ljJUWN7es5TzHtPUn/lfQDSXPl/XNL+oekZ/L+iZKulrRWlfOtKulGSe9KelnSb8oL/Sn5paQXcnm3SlqzV94QMzOzBtWfWkymAltWSX+q8PxY4CJgfmAH4GRS8HUSaUK1AI4EngYWJs3SepOkz0XEMwCShgI3AI8A2wMr5HLnAn5VONeBwCHA/sBjwM+AGySt7hWGzczMOqc/BSazIuKuNvJMKOS5SdKqpMnOToqI90izvH5E0g2klYV3AI7LyfsA8wE7RcQ04HpJCwNjJf0hIqZJmpcUmBwZESflsu4krUD8Iz4ewJiZmVk79YuunC64DxjRyv53gPeBuQtpWwHX5qCk4nxSsLJRfr0eqcXlH5UMEfEOaWbZrbpcazMzsybVrwITSQPLWxuHjAA+1q2Sx4YMlPQJ4A/AB8B5hSwrk7pmPhIRzwPv5n2VPB8AT5bO92ghj5mZmXVQfwpMhgMzy5ukEYU8c+WgYyFJewM7A5eWyjkgH/sK8A1g64h4rrB/KDClyvkn532VPG9HxAdV8swvaW7MzMysw/rTGJOpwGZV0l8uPD8xb5AGup4FjC3lP4M0uHUJ4AfAlZI2jIhHurOyZZLGAGMAFhs2b0+eyszMrN/qT4HJrIhoaSPP0aRxH+8Bz+QBrx+T75h5FUDS1cDDpIGse+csk0mrFZcNzfsqeRaUNKDUajIUeDciZlQ57zhgHMCnRgz2yolmZmZV9KfApD2eb0fw8pGImCXpv8DyheTHKI0TkbQM6Rbkxwp5BgArAo8Xss4xPsXMzMzarz+NMel2+bbfzwPPFpKvBraQtFAhbXdSK8wt+fUdwDRg10JZ8wPb5ePNzMysE/pTi8lASetUSX8hIl5q62BJo0i38l5DGpdSGWOyBLPnMAH4C/Bj4BJJR5FaU8YCx1VuIY6I9yX9HjhE0mRmT7A2F/Cnzl2emZmZ9afAZDBwZ5X0Q4DD23H8Y8DXSUHIUNJdOXcDIyPi4UqmiJgsaVPSbLFXkO7QOZ45B9H+nhSIHES6Y6gF+EpEvNbuKzIzM7OPUYTHYfa2T40YHH86ZP16V8PMzEq8iF/PkXRfRIxsK19/ajFpGAsvspJ/+M3MzKpo6sGvZmZm1rc4MDEzM7M+w4GJmZmZ9RkeY1IHk994kotO37Le1TAzayq7fPOaelfB2qHhW0wkjZd0UY19LZLOyM/PkBSF7S1J90raqcaxO0u6SdIUSdMlPSHpOElL9uDlmJmZNbSGD0w66DFg3bztDDwJXChpg2ImSceS1uR5BtgL2Jw018mmwMm9WWEzM7NG4q6cj3snIu6qvJB0A7AJ8FXg9py2HWmW129HxGmFY2+RNI4UpJiZmVknuMWkFRHxIfAuMKiQvC9wfykoqeT/ICK8Vo6ZmVknOTApkTQwb8Mk/RwYAVyW9w0C1iOtt2NmZmbdzF05H7cWMLPw+kPgFxExPr8eDswDPN/L9TIzM2sKDkw+7lFg7/x8fuBLwBGSJkXEGYV8HV5gSNIYYAzAIsPn7WI1zczMGlMzBCazgAE19g3I+yvejYiWwutbJS0OHC3pTGASMB1YtqOViIhxwDiAFUYM9sqJZmZmVTTDGJOJwCdq7FsCeL2N4x8FFgEWiYiZwL+BLbqvemZmZlbRDIHJbcBakpYqJkr6IrB43t+a1YH3SK0lACcAIyV9o5xR0lySPKWrmZlZJzVDV85ZpHlHbpV0OPAcsApwKHAHcG0h7wKS1snP5yONMfkucEq+dZiIuELSccCpktYn3bHzNrAysA8wAd+1Y2Zm1ikNH5hExNuSNgR+B/weGAa8BlwAHFwJOLKVgTvz8/eBZ4FfA8eVytxP0h3Aj4C/k4KYCcDlwDE9djFmZmYNruEDE4CIeBkY3Uae0W3lKeW/GLi4K/UyMzOzj2uKwKSvGbrISl7l0szMrIpmGPxqZmZm/YQDEzMzM+szHJiYmZlZn+ExJnXwxqQnOP3MzetdDTOzfueb37iu3lWwHtZnW0wkjZUUhe1dSf/Na85U8owo5Xlb0n8kfadGmTtLuknSFEnTJT0h6ThJS1bJOzqXeU6Nsr4i6TxJE3K+sd128WZmZk2qzwYm2VRg3bxtB9wI/FXS10r5fp7z7Aj8B/g/SV8vZpB0LPAP4BlgL2Bz4HhgU+DkKucelR+3lzRflf1bAp/NdXq3w1dmZmZmc+jrXTmzIuKuwusbJa0H7ECa2Kzi8Uo+STcAI0mrBJ+T07Yjzf767Yg4rXDcLZLGkYKUj0hajBSw3JgftyMFNUX7R8R+Of/2XblIMzMzS/p6i0k1bwGDau2MiAD+CyxTSN4XuL8UlFTyfxARV5eSdyWtPPwj4CVmt54Uj/uwnGZmZmZd0+cDE0kD87Zw7p7ZCLi0jcOWJU0nj6RBwHp0bP2aUcADEfEYaer6rSQN7njtzczMrCP6emAyHJiZt6nA2cDJEXFWKd9cOXgZKumnwOdJ6+JUypgHeL49J5S0LCmQOT8nnZeP36kL12FmZmbt0NfHmEwFNsvP5wHWAn4j6c2IOKyQ77LScT+JiFtLadHOc+6RHy8AiIgWSU+RWlFOb3fNS/LdRGMAhg+ft7PFmJmZNbS+HpjMioiWwut/SxoIHCnpT4X0fYHbgcWAg4FjJN0SEf8BJgHTSd077TEKuB+YKmlITrsc+ImkxSPitc5cSESMA8YBjPjkwu0NkszMzJpKX+/KqeZRYG5ghULaUxHREhH/ArYFppG7ciJiJvBvYIu2Cpa0MrAmqWVmcmH7GWkw7K7ddhVmZmY2h/4YmKyeH1+otjMiJgNHAVtK+mxOPgEYKekb5fyS5pK0ZX45CvgA2AbYpLQ9RJW7c8zMzKz79PWunIGS1snP5ya1ZPwKuCwiXpU0osZxfwYOBPYH9oqIKyQdB5wqaX3SmJS3gZWBfYAJpLt2RgHX55aXj5F0JqmLaLmIeE7ScsDahbqtKmkX4J0qtx+bmZlZO/T1FpPBwJ15uwH4AfAX0sytNUXE28CJwB6Slslp+wG7AyuRJme7HtiPNIna9yWtlfedXaPY84APmT04dhPgwrwtROrmuZAUFJmZmVknKM1HZr1pxCcXjkPHrtN2RjMz+xgv4td/SbovIka2la+vd+U0pEWGf8q/XGZmZlX09a4cMzMzayIOTMzMzKzPcGBiZmZmfYbHmNTB628+yR/PbXO+NzOzpvXjPa+tdxWsTvp1i4mkmyT9J09TX0zfWVJI+oqkjfPzyjZZ0u2SNq1S3vqSrpL0pqT3JD0k6Wd5heJivkUl/VHSPZJmSJrQw5dqZmbWFPp1YAL8EFgF+HElQdKCpJle/xER1xfy7gmsC3wdeB+4RtKaheO+BtySX34L2Bq4FDgcuFTSgEJZS5HmRHkVeLA7L8jMzKyZ9evAJCIeBY4FxkpaMicfRpqYbd9S9oci4q6IuArYgTTz63cBJC1FWmDvoojYJiL+GRE3R8ShwM6kKer/X6msxSPiq0B5FWMzMzPrpH4dmGS/Bd4ETshr4/wYODQiXq51QJ4Z9glgRE76DjAv8Msqea8GxlNolYmID7up7mZmZlbQ7wOTiHgX+AlpSvjLgEeAP7Z2TO6WWYbUFQOwIakV5Jkah/wT+KSkpbujzmZmZlZdvw9MACLiMuA+UgvITyPigyrZBkgaKGkx4DhgCdIYEkhjRp5r5RTPFfKZmZlZD2mI24UljQQ+BwSwMXBzlWwPFp6/AxwQEVf2eOUySWOAMQBDh8/bW6c1MzPrV/p9YCJpLtKKvneSVgw+UNKZVbpl9gCeBiYDz0XErMK+l4DlWjnNcoV8nRIR40gDbFl2+cFeOdHMzKyKRujK2YfUWvID4Pek4KHaGJOHI6IlIp4uBSWQ7qz5jKQRNc7xVeDZiHixm+psZmZmVfTrwCSPFzkC+FNEPBQR00l3z2wjafsOFPU3YHouq3yOzYEvAyd2Q5XNzMysFf29K+cY4D3g0EpCRPxL0mWk24eva08hEfFSHgNytqSFSYHKVGAj4BfAVcBJxWMk7ZKffgqYv/D6loiY2IVrMjMza1r9NjCRtCGwF/C1iJhW2v0T0m3DvwRubE95EfF3Sc/lY84A5gOeBA4htciU7/S5sMbrTUjznpiZmVkH9dvAJCJuBVRj33PAAoWkqvmqHPdv0iyv7cnbrjLNzMys/fptYNKfLTZsJa+caWZmVkW/HvxqZmZmjcWBiZmZmfUZDkzMzMysz/AYkzp4efKTjP3HFvWuhplZnzJ2N4+9swZoMZG0g6TrJE2SNEPSS5IukrRlIc8ESZG3GZKelHSUpAVKZY0t5AtJ70r6b57jpJjv05JOlvRozvOMpBMlDemlyzYzM2tI/brFRNLxpJlezyKtlzOJtK7NHsDVklaMiKdz9r8DfwLmJk2cdggwHPhOqdipQCWoWQDYDvirpLcj4u85/SvA+vmcDwHLA4cD60paJyI+7O5rNTMzawb9NjDJU87/FPhmRJxR2n22pO1Is8JWvBIRd+Xnt0paCviGpDGlQGJWIR/AjZLWA3YgBTcA5wEnR0RlMb7xkl4ErgW+BNzStaszMzNrTv02MCEFJfdWCUoAiIgr2jj+P8C8wKLAa23kfQsYVCh7UpU8D+THJdsoy8zMzGrol2NMJA0E1gXatRZODcuSAo43qpWft4UlfZ3U9XNpG+Wtmx+f6EKdzMzMmlp/bTEZDswDvFBMlCRgQCHpg0J3i3JAMzewIbAPcESVNXCGAzNLaX+MiLNqVUbS/MBRpAX87uvoxZiZmVnSL1tMCqL0ej9SUFHZfljY97Oc9g5wNXBTRBxVpcypwNp524C0IOA3JB1aJW8lGDoVWAz4Vq2KShojqUVSy7vTZrTj0szMzJpPf20xmQRMB5YupZ/N7JV97y3tOwc4kXSnzTeAb0r6fkT8uZRvVkS0FF7/O7e0HCnpTxHxZin/UcCOwFci4plaFY6IccA4gCVXGFwOqMzMzIx+2mISEbOAO4HNS+mvRURLKbCoqOy7JSK+BdwE/KY8l0kNj5K6gFYoJkraF/g5sHdE3NaZazEzM7PZ+mVgkp0AfFHSXp08/iBgEeDb7ci7en78aEyLpD2BY4GfRcQ/OlkHMzMzK+ivXTlExGWSTgDOkLQJcAXpDpvhzG5JebuV4++RdD2wr6STC4NgB0paJz+fG1gL+BVwWUS8CiBpI+B00l1BdxXyA7wYES92y0WamZk1mX4bmABExL6SbgV+QBqAuhAwkdTNs3VEXN1GEYeTJkPbjTRpGsDgfDykwbLPAX/JeSs2Ic1rskXeig4DxnbicszMzJqeZt9Na71lyRUGx5gj12k7o5lZE/Eifo1N0n0RMbKtfP15jImZmZk1mH7dldNfLTl0Jf9nYGZmVoVbTMzMzKzPcGBiZmZmfYa7cupgwpQn+ealW9a7GmZmveb0Ha+pdxWsn+hTLSaSxkqaY7XfvO8MSS35+caSorC9JekxSf8naY1Wyh+d85/TSp7FJZ0g6WlJ0yVNlnSdpF0KeVaU9FdJD0n6QNL4Lly2mZmZZf29xWRP4BlgfmAl4JtAi6R9IuLUKvlH5cftJc0XEe8Vd0r6NHAzaaG/Y4BHgIWBrYFzJT0ZEf8BVstpd5HmMzEzM7Nu0N8Dk4ci4n/5+U2S/g84DfizpFsi4qlKRkmLAZsCN+bH7YDyVPLnAm8C60XEtEL6FZL+DEypvI6Iy3K5F5GmtjczM7Mu6lNdOV0VER8C+wIfAN8p7d4VGAD8CHiJ2a0nAEjakDT9/EGloKRS9kMR8XzhPGZmZtbN+mRgImlgeQPUnmMjYjLQApSnVh0FPBARjwEXAFtJGlzYvxEpoLmh61dgZmZmndEXA5PhpDVqytveHSjjRWDxygtJywLrAefnpPOAeYCdCscsBUwsjzsxMzOz3tMXx5hMBTarkn4osEQ7yyi3ruyRHy8AiIgWSU+RWlFOL+TrsYWDJI0BxgAssOi8PXUaMzOzfq0vBiazIqKlnChpEu0PTJYCXiu8HgXcD0yVNCSnXQ78RNLiEfEaadzJopLmjYj3O137GiJiHDAOYJEVB3vlRDMzsyr6YldOl0gaCowE7syvVwbWJA1snVzYfkYaDLtrPnQ8KVDbtFcrbGZmZh9pqMBE0lzA8aSAozKPySjSoNZtgE1K20N5PxFxG3Af8DtJC1Up+zOSlunpazAzM2tmfbErpyM+K2lBYF7gU6QJ1kYC3yvMYTIKuD4i/lU+WNKZwDGSlouI50gTtt1MmqTteGZPsLYF8F3gi8ALkuYnTbAGqdto4cLMsP+KiHd74FrNzMwaXn8PTM7Nj++S7sS5Fdgnz86KpLVIM8KOrXH8ecAfSINjj4qIxyV9HjgI+AUp6HgXuAf4WqVcYDHgwlJZldefBCZ06arMzMyalCI8DrO3LbLi4Nju6HXrXQ0zs17jRfxM0n0RMbKtfA01xsTMzMz6t/7eldMvjRiykv97MDMzq8ItJmZmZtZnODAxMzOzPsNdOXXw5JTn2eqyH9a7GmZm3e7q7U+udxWsn3OLiZmZmfUZ7QpMJI2V9EaNfWdIaim83kDS9ZImSnpH0pM5z9KFPOMlRd5mSXpa0vGSFi7kkaSDJT0v6X1J90vaosr5B0s6XdJkSVMlnStpeI26fjKf83lJ5YX+ql1zSDq8xv7dJV0i6ZWcb3Rr5ZmZmVnburXFRNIGpDVnpgLfBnYATgJWAZYrZb8ZWBfYOOf5LnBaYf+BwK+Bk4HtgYeBKyStXSrnH7mM7wCjgbWBf9aoYmWV4WWADdq4nFGlY8p2AUYAV7ZRjpmZmbVTd48x+T7wKLBrzJ657XrgxCotFG9GxF35+e2SFgB+K2lRUmBzEPCHiDgq57lW0qrAocC2AJLWBTYHNoqIW3PaS8DdkjaLiBtK5xwF3AV8Jj+/rdpF5NlfPwXcCGwq6QsRcU8p2+4R8WGeEv87bb81ZmZm1pbuHmMyBHg9qkwnWy2t5L78OAJYAViIFNQUXQd8RdLc+fVWwGuVoCSf5x7g2bzvI5JWIwUkpwOXA7tKqhWYjQLeJ7XAvM/s1pPi9XzYxvWYmZlZB3UoMJE0sLwBxZaQ+4FNJB0iafkO1mVEfnyVtCgfwIxSnhnA3ECl7JWBx6qU9WjeVzQKmAlcTFojZxFgs/KBuWVnd+DqiHgR+BewW1652MzMzHpQR/7YDif9YS9vexfyHE0aY/Ib4GlJL0v6i6RPVSlPObiZR9KXgIOBFtJifM8AQRovUvSF/DgsPw4FplQpe3LeV7QHcENETAKuzXnmaAkhjT1ZBjg/vz4PWBLYqEpeMzMz60YdCUymkgKF8vbR4M+ImAZsCqwH/A54mjT+4v48bqNoJ1Jg8z5pVeAJwJ6RTCUFBAdL2kTSMEn/j9ktHB3qRpH0BVL30Pm5njOAS4AdJM1byj4KeKdwXVcBb1E9iOlIHcZIapHUMmPae10pyszMrGF1JDCZFREt5Q2YVMyUA4s7I+LgiPgSMJIUSBxSKu8mUmDzOWBYRGwQEU8U9v8UeCTnmwTsD1Ru3X01P04GBlep69C8r6LSjTNe0hBJQ0gBx8LANpVMuWtqV9JYlrlzvnlILSw7SxrU2hvUmogYFxEjI2Lk3AvP19lizMzMGlqPj5uIiAdJg1jLYz4m5+DmwYiYXOW4iRHxZVK3yuqkcSXvAK9GxISc7bEq5UJh7EkeG7IbMAh4jhSwTCa1mMDHW0I2I4092bGQbzLp1uBhwBzzqJiZmVn36dbbhSUtFhGvl9JE6kZ5rTNl5gGoL+Yul2/x8blOrgYOkbRBRNyezzeSFMRcnfNsRBojcgBQvuX3m6SBrQtFRKW75k1g5ypVuSDv97wlZmZmPaS75zH5W26huJg0vmQo6Y//GqQuknaTtBepleMZYFlgX+AD4MhKnoi4U9J1wFmSfk7qMjoKuL0wh8ko0gDZEyNieukc00iDd3eU9A/ShHB/j4jxVepzAfAtSfNHxLt5TpVVmX0H0UhJbwMTI+KWjlyrmZmZJd3dlXMK8DZpxtbrgL+S5iPZIiIu6kTdDgCuId3tcw+wcUS8Xcq3O3ALqSXlLNJ8KDsC5DEhOwMXloMSgIi4n3Rr8SjSWJOFgbNr1OccYAFgu/x6N+DCQv4f5teHdfA6zczMLFPb855Zdxu84mKx3rEdakAyM+sXvLqw1SLpvogY2Va+7u7KsXZYaciy/uU1MzOrwrOZmpmZWZ/hwMTMzMz6DHfl1MGTU15h60sPbzujmVkf9q8df1XvKlgDcouJmZmZ9RkNGZhIelZSSFqxlL5xTl+9lL5PTm/PNkLSopL+KOkeSTMkTejVCzQzM2tQDReYSFoXGJFftrnwnqTRpPlXfg+sW9j2zFl+WEp/BViKNH/Kq8CD3VV3MzOzZteIY0wqqwP/Lz//ba2MkkYBp5JmhT2otK8ykdsjEXFXad9DEbF4fn4MaS0dMzMz66KGajGRNIA0I+vlpJlgV5G0Ro28O5Fmiv1rROzbkfNExIddrauZmZnNqaECE2ATYHHgfOAiYCbVu3O2yXnOInXVmJmZWR/QaIFJZcG+ayLiTdJ6PXvkFY6Lfg88BHw3PCe/mZlZn9EwgYmkuYGdgEsjYkZOPh9YjjRoteg6YC1gdC/Wb4ykFkktM6a901unNTMz61caJjABtgKGAP+SNETSEGA8MJ05u3P2By4Axknajl4QEeMiYmREjJx74QV645RmZmb9TiMFJpXg40Jgct5eAOYBds0DYys+BPYGbgYukLR+b1bUzMzMqmuI24UlLQBsB5wHjCvt/hxwHPBl0mBYACJiRr4z52bgCklfioiHe6nKZmZmVkVDBCbA9sD8pPlI7i7ukPRv4GBSi8pZxX0R8ZakrYF/A9dKWi8inm/PCSVV5i75FDB/4fUtETGx85diZmbWvBqlK2cU8GQ5KAGIiJnAP0gDY+epsv91YHNgACk4Gd7Oc16Yt+2ARQuvV+vMBZiZmVmDtJhERKsDWCPiB8AP8svyrcNExLPAEqW0/1XLW9hfc5+ZmZl1TkMEJv3NSkOW8HLhZmZmVTRKV46ZmZk1AAcmZmZm1mc4MDEzM7M+w2NM6uDJKa+zzSV/rHc1zMza5aqdflzvKlgTqVuLiaSdJd0kaYqk6ZKekHScpCXz/ihs70l6VNIBkgaWyjmjlLe4bZDzjCilfyDpeUn/J2nRUnnjS3knS7pF0salfKNL+V6XdK2kz/fsO2dmZta46tJiIulY4KfA6cDxwDRgVWAf4JPAjjnrscBFwHzAtqRVgQcBh5eKfAz4ZpVTlWdy/TlpMrUBwCrAEaRF/jYv5bsZ+GV+Phz4MWkNns9GxFOlvF8G3iPdbnwIcLOkVSLi5epXb2ZmZrX0emCSF837GfDtiDitsOsWSeP4eJAwISLuys9vlrQaaY2bcmDyTiFfax4v5Pt3XpH4JEkLRsTbhXxvFsuTdCswJdetHJjcWzlWUgvwHLAncHQ76mNmZmYF9ejK2Re4vxSUABARH0TE1a0c+x9gmW6sy1ukSdQGtJHvPWAWqbWmpoh4AZgIjOiOypmZmTWbXm0xkTQIWI/URdMZywLP1ih7jmuJiFmlpLlyvgHAysD+wM0RMXXO4j4qbzhwIBBAa0ETkhYChgGvtnEdZmZmVkVvd+UMJ61X066F8pgdSFTGmOwEfKNKvrUorBxcUJ42/rLS60eAvaoct1OpvOnANyPiiSp5B+Q6LgEck895RZV8ZmZm1oZ63S4c7cx3Yt4qjo+I86vke5Q09qQt+wK3k4KHpYADgKvzqsLFMSY35X0ACwFbA6dLeiMiri+VOaXw/A3gWxHxYPnEksYAYwDmXWRoO6pqZmbWfHo7MJlEan1Ytp35jyatDDyYdBfPvpJuiIh/lfK9GxEt7SjvqUK+eyX9m9TtMho4qZBvcqm8myV9GjgSKAcmGwLvkoKSFyLiw2onjohxwDiAwSsu297AzMzMrKn06uDXiJhJul13i3Ye8nxEtETEjaTulceBoyV1y8q+ETGRFFCs0o7sj9bI90BE3BcRz9UKSszMzKx96nFXzgnASElzjBWRNJekLasdlIOaQ0jznWzXHRWRtDiwCPBCO7Kv3s58ZmZm1km9PsYkIq6QdBxwqqT1SQNS3ybdJbMPMAG4psbhF5MmU9sfuLyQvoCkdarkfyoi3ii8/rSkN5g9xmR/0i3D55WOG1YorzLGZGvSGBUzMzPrIXUZ/BoR+0m6A/gR8HfSXTcTSMHGMa0c96GkI4EzJa1TmARtZeDOKofsBZxTeF0s+zWgBfheRDxXOm6TQnnvkCZV+x7wf21fnZmZmXWWIjwOs7cNXnHZ2OAPP693NczM2sWL+Fl3kHRfRIxsK59XF66DlYYs5l90MzOzKuq2urCZmZlZmQMTMzMz6zMcmJiZmVmf4TEmdfDk5DfY5uK/1bsaZtZErtr5O/Wuglm7uMWkBknPSgpJK5bSN87plW2qpLsl7VCnqpqZmTUMByZVSFoXGJFfjqqRbU9g3bx/EnCJpA17vnZmZmaNy4FJdaNIE6vdTe3A5KGIuCsvKLgLMBn4ei/Vz8zMrCE5MCmRNADYjTQL7WnAKpLWaO2YiHiXNDvsMj1fQzMzs8blwGROmwCLA+cDFwEzqd1qAqTFB4GlgWd7vHZmZmYNzIHJnEYBU4BrIuJN4DpgD0kq5RsgaaCkRYGjgAWAE3u1pmZmZg3GtwsXSJob2Am4NCJm5OTzgbNJA13vKGR/sPD8Q2DniHi8lbLHAGMA5l1kWDfW2szMrHG4xeTjtgKGAP+SNETSEGA8MJ05u3P2ANYGdgYeB06XtGStgiNiXESMjIiRcy+8UA9U3czMrP9zYPJxleDjQtJdNpOBF4B5gF3zwNiKhyOiJSIuAbYD5gcO6c3KmpmZNRoHJpmkBUgBxnmkAbDF7WekAbFfrnZsRDwN/A0YLWmxXqmwmZlZA3JgMtv2pFaPEyNifHEDTiJNotba3Tl/AAYA/6/Ha2pmZtagHJjMNgp4MiLuLu+IiJnAP0gDY+epdnBEPAecA/wgt76YmZlZB/munCwitmtj/w+AH+SX5VuHK3m+1d31MjMzayYOTOpgpaGLeKVPMzOzKtyVY2ZmZn2GAxMzMzPrMxyYmJmZWZ/hMSZ18NTkN9n2onPrXQ0z62eu3GXPelfBrMc1VIuJpJ0l3SRpiqTpkp6QdJykJSVtLCkkrV7luG3zvhH59Yj8ettWznWYpP9KmibpLUktknbvwcszMzNreA3TYiLpWOCnwOnA8cA0YFVgH+CTdP/KvwsDZwCPAB8AuwDnS/ogIi7q5nOZmZk1hYYITCRtR5o2/tsRcVph1y2SxgGbd/c5I2LfUtJ1klYD9gYcmJiZmXVCo3Tl7AvcXwpKAIiIDyLi6l6qxyRg7l46l5mZWcPp94GJpEHAesA1dTr/QElDJO1Japn5Sz3qYWZm1ggaoStnOGn9mud7+8SS1gHuzC9nAT+KiH/2dj3MzMwaRSMEJhVRh3P+F1gbGAJsA5wkaVpEnFfOKGkMMAZgvkWG92YdzczM+o1GCEwmAdOBZdvINys/Dqiyb0ApT7tExDtAS355g6TBwFHAHIFJRIwDxgEMWWH5egRRZmZmfV6/H2MSETOBfwNbtJF1Yn78RJV9SwAfkoKcrrgfWEZSIwR8ZmZmva7fBybZCcBISd8o75A0l6QtgSeBV4Htqxy/PdASEe91sR7rAy9GRIdaXszMzCxpiP/sI+IKSccBp0paH7gMeBtYmTTB2oSIuEbSYcApkgK4ChgEfI10N812VYpeX9K8pbQJpNaX04DzgaeBBYEdgT2A73fz5ZmZmTWNhghMACJiP0l3AD8C/g7MRwoiLgeOyXn+Iult0rwn3ybN2PoAsG2NuU4OrJJ2JvAT4GXgl6RuoCmkGWC3iYh/dd9VmZmZNZeGCUwAIuJi4OI28pwDnNNGngmA2jjdXh2qnJmZmbWpoQKT/mLFocO8SqiZmVkVjTL41czMzBqAAxMzMzPrMxyYmJmZWZ/hMSZ18NTkKWx30SX1roaZ9VFX7LJTvatgVjcN32IiaaykkPRkjf1P5v1jJV0s6ekqc5cg6VpJj0qaO79eU9IFkl6VNEPSy5LOlbR2T1+TmZlZo2r4wCR7H/ikpJHFxBxEjMj7Ic1PshhwUCnfLqRJ2L4fETMk7QTcQ1rZeF9gM2A/YDBwXc9dhpmZWWNrlq6cd0jr2OzB7EX3yK9vAtYCiIgXJY0FjpB0dkQ8JWkB4HjgrIgYL2lJ0iRr5wGjI6K4IN95krbt+csxMzNrTM3SYgJp+vjdJAkgP+6W04tOBB4H/pRfHwrMD/w8v/4OMDewXykoASAiruz+qpuZmTWHZgpMLgEWBzbIr78ELJrTP5IX4Ps+sIWkQ4CfAgdGRGV14o1IC/690RuVNjMzayZNE5hExBTgGlL3DfnxmoiYWiXvHcCpwG+Ae4G/FXYvBTzfo5U1MzNrUk0TmGTnA7tImgfYhTm7cYqOzo/HVumymaMLpy2SxkhqkdQyY9ocsZCZmZnRfIHJ5cCCwBHAAsAVreSdUXqseAlYtqMnjohxETEyIkbOvfDgjh5uZmbWFJoqMImId4ArSbf4XpFfd9R4YKSkYd1ZNzMzM2uywCT7M6ml5C+dPP5UYCZwTLWdkrbpZLlmZmZNr1nmMflIRIwntXp09viXJY0mzVmyNHAaqXtnKdKA2g0Bt6aYmZl1QjO2mHRZRFwMfBGYQpr35CbgOOBt0iywZmZm1gkN32ISEWOBsW3kWaRK2gRArRzzAGmCNjMzM+smDR+Y9EUrDh3i1UPNzMyqcFeOmZmZ9RkOTMzMzKzPcGBiZmZmfYbHmNTBU5Onsf1F19S7GmZWZ5ftsmW9q2DW5zRVi4mk0ZLuk/SWpMmSHpB0XClPFLb3JD0q6QBJcwRxkjaWdKWkNyTNkDRB0jhJn+69qzIzM2scTROYSDqItErwtcBOwN7AZcBXq2Q/FlgX2Br4F/B74MBSeT8mzV/yHvA90vwlhwGr0PrigGZmZlZDM3Xl/Aj4a0T8spB2haTDquSdEBF35ec3S1qNFMgcDiDpc6QJ1Q6PiF8XjrsVOF3Stt1ffTMzs8bXNC0mwBDg1XJiREQ7jv0PsEzh9f8D3gB+Wy1zRFzZifqZmZk1vWYKTO4H/p+kb0ga3sFjlwWeLbzeCLgxImZ2W+3MzMysqQKTH5LWsjkDmCjpYUm/kbRwlbxzSRooaSFJo0hjUn5T2L8U8HyP19jMzKzJNE1gEhEPkQamfhU4hbQOziFAi6QFS9lPBGYC04C/AydHRHlAa3u6gD4iaYykFkktM6ZN7cwlmJmZNbymCUwAImJ6RFwRET+KiFWB7wArAd8uZT0aWJt0p82VwL6Sti7sf4nUvdORc4+LiJERMXLuhQd3/iLMzMwaWFMFJmURcSrwJrByadfzEdESETeSunEeB46WVFlteDywabW5TczMzKzzmiYwkbRYlbRFgcHAa7WOywNcDwFWBbbLyScBiwIH1zjX1tXSzczMrHXN9B//fyVdBlwHvA4sB/wceBc4s41jLwYeA/YHLo+IByT9DDhB0qqkCdXeAD4JfIsU7PyrR67CzMysgTVNiwnprpoRwB9JwclvgYeBL0TEs60cR0R8CBwJbCBpnZz2R2BTYEHSjLI35nM8DuzaM5dgZmbW2JqmxSQiTgZObkc+1Ug/CzirlHYzcHO3VNDMzMyaqsXEzMzM+rimaTHpS1YcurCXOzczM6vCLSZmZmbWZzgwMTMzsz7DXTl18PTkt9nx4tvrXQ0zq7NLd96g3lUw63Pq0mIiaaykqLF9PeeptX+DVvYVtxGSzpDUUqMOF0kaX6NOH0qaLOleSUdI+kTp2CUkHS3pP5LelvSCpDMlLdmjb5yZmVmDq2eLyVSg2gjQpwrPjwUuKu1/FFi38Hp54FzS6sH3F9Jf6WKdBgOfB74PjJG0ZUTcl/etBexImr/kbmBxYCxwh6TVI+LtTpzbzMys6dUzMJkVEXe1kWdCjTwfpUmqBAGPlPPOXtqm03W6VtKfgVuB8yWtHBEfALcDK0fErMK57idNrrYzbc8ka2ZmZlV48GsbImIK8AtgReArlbRiUJLTniBNb+/uHDMzs06qa2AiaWB5K2WZq7R/QF0qmlYTngWsUyuDpM8C8wNP9FKdzMzMGk49A5PhwMzyJmlEIc+Jpf239HIdAYiI90mL9C1ebb+kuUh1fRK4vBerZmZm1lDqPfh1syrpLxeeHw38o/D6rR6tUetaG7ByJGlA7kYRMbPqwdIYYAzAfItUjW/MzMyaXr0Hv1a9lbfg+XbkafUcQK3unwF5f5skzUtq4Xmtyr4fAPsDoyLi7lplRMQ4YBzA0BVWjvac18zMrNk0+uDXicAnauxbAni9neVsQgri7iwmStoZ+BPwi4i4oLOVNDMzs6TRA5PbgE9I+kIxUdLSpLlIbmurAElDgKNI86vcUEjfmDR/yp8i4phuq7GZmVkTq2dXzkBJ1e5yeSEiXuqmc1wD3AFcKekw0uRsywG/Ap4Dzm6lTguRgpfvk+622TLPYYKkVYB/Ao8BF5SuY2JEPN1N9TczM2sq9QxMBlPqGskOAQ7vjhNExIeStgZ+CxxI6tZ5kxSwHFRlhtZKnQKYRmolOYfUKvJqId8Xc941SIFP0ZnA6O6ov5mZWbNRhMdh9rahK6wcG//hb/WuhpnVmRfxs2Yi6b6IGNlWvkYfY2JmZmb9SD27cprWCkMX9H9KZmZmVbjFxMzMzPoMByZmZmbWZ7grpw6emfweu178UL2rYWbd7MKdP1vvKpj1e24xMTMzsz6jroGJpIslPZ3Xoinvu1bSo5JC0galfd/P6b8qpa+U07+WX4+V9EaNc58hqeo6PHlfSPpOlX3j875a24QOvAVmZmZWUO8Wk58AiwEHFRMl7QJsTloc7wNgvdJx6wHv1kgH+HdnK5SDpB3zy1FVsvyAtJJweft/ef+/OntuMzOzZlfXMSYR8aKkscARks6OiKckLQAcD5wVEVdKeog5A5B1gbOA3SUpZs8Stx7wUkQ814VqbQ0sDNwIbCJpiYh4pVDnR8oH5DqfRZryfr8unNvMzKyp1bvFBOBE4HHSKr0Ah5LWpvl5fn0HKRABQNJiwAr5uIWBVQplrcecU8R31CjgJeBHpPdnt3YccyKwLDAqIt7r4vnNzMyaVt0Dk4iYRVoobwtJhwA/BQ6MiIk5yx3AYpJWzK/XJbWKPAZ81JoiaTCwKlW6cSQNLG+AquRbCNgG+Ecu/36qd+cUj9kJ+DZp7Z3/dOzqzczMrKjugQlARNwBnAr8BrgXKC4kUwk01is8Vhb/u7OQvg7pesotJsOBmVW2vatUZQdgPuD8/Po84IuSlq9Wb0lLAf8HXAucUPsKzczMrD36RGCSHZ0fjy2MGSGPF3mJ6oHJXaX0d4EHSuVOBdausl1ZpQ6jgGci4p78+gLSSsN7lDNKEmlcyQfAN6KN1RAljZHUIqll+rTJrWU1MzNrWn0pMJlReiy6E1hP0iBgJLNbRe4EPiVpOCkwuSd3DRXNioiW8gZMKmaStAjwFeAKSUMkDQHeIrXgVOvO2R/4MjA6Il5r6+IiYlxEjIyIkfMsPLSt7GZmZk2pLwUmrfk3sBqwEWlsyP0AEfEU8AawPvBFujbwdRfSXUo/ASYXti8Aq0tavZJR0ueB3wJ/igjfHmxmZtZN+suU9HeQgqh9gfsjotiqchfwPWAhujB/CalV5FHSPCVF8wBX5P0HS5of+DvpTqJfdOF8ZmZmVtJfApMHgPeArUhznBTdCRxBGgtyJ50gaWngS6Q7a8ZX2X8NaZzJwcCxwKeB7wJrpqEmHxcRd3WmHmZmZs2uXwQmETFT0r3AhswZfNxJ6t55JCI6O6p0d1Jgc26N/ecAF0j6Iik4gnQ3Ti1zRitmZmbWJrVxM4n1gGErrBab/uG8elfDzLqZVxc2q03SfRExsq18/aLFpNEsP3Q+f4GZmZlV0V/uyjEzM7Mm4MDEzMzM+gx35dTBC1Nm8ONLX6h3NcysG/xxx2XqXQWzhuIWEzMzM+sz+kVgImmspJD0ZI39T+b9Y0v5Q9KHkiZLulfSEZI+UeX4St6vV9n39cr+QtoSko6W9B9Jb0t6QdKZkpbsxss2MzNrOv0iMMneBz4p6WO3GklaGxiR9xdNBdYlraGzB3AJsBfwX0lrVSn/baos1kea8fXtUtpawI6k1Ye3I62b80XgDkkLtv+SzMzMrKg/jTF5h7RGzh5ASyF9D+AmUrBQNKs0A+u1kv4M3AqcL2nliPigsP8KYBdJQysTtUkaRlrY70Lga4W8twMrFxcMlHQ/aZr6nYEzO3+ZZmZmzas/tZgAnA/spjwPfH7cLae3KSKmkNa3WZEUcBTdCbxMCiwqds5pH5ttNiKmlFcxjogngHcBd+eYmZl1Un8LTC4BFgc2yK+/BCya09trPDALWKeUHsAFpK6bilG0M+iR9FlgfuCJDtTFzMzMCvpVYJJbPCoL6pEfr4mIqR0o433gDVKAU3Y+sJGkxfMg2Y1oR2AiaS7gROBJ4PL21sXMzMw+rl8FJtn5pLEg8wC70M4WjZKqi+xFxAPAU6Tuod2AJyLiwXaUdyRpoO1eETGz6gmlMZJaJLW8N+3NTlTZzMys8fXHwORyYEHgCGAB0qDVdpM0LzAceK1GlgtILTF75OdtlfcD0l0534iIu2vli4hxETEyIkbOt/CwjlTZzMysafS7wCQi3gGuBPYFrsivO2IT0t1Id9bYfz6p9WMd2miNkbQz8CfgFxHRZhBjZmZmretPtwsX/RmYB/hLRw6SNAQ4itRdc0O1PBHxqKRx+fljrZS1MXAu8KeIOKYj9TAzM7Pq+mVgEhHjSXfXtGagpMqdNwuR5jn5PunOmS1Lc5iUy9+ntYIlrQL8E3gMuKBwHoCJEfF0G3UzMzOzKvplYNJOg0ndNQFMI7WSnENq4Xi1i2V/MZe/BnBHad+ZwOgulm9mZtaUFBFt57JutfiKn43dj76q3tUws27g1YXN2kfSfRExsq18jdxi0mctM2Ruf5mZmZlV0e/uyjEzM7PG5cDEzMzM+gwHJmZmZtZneIxJHbw+ZSYnX1pr4lkz609+uGO1ZbfMrLPa1WIiaaykN2rsO0NSS+H1BpKulzRR0juSnsx5li7kGS8p8jZL0tOSjpe0cCGPJB0s6XlJ70u6X9IWVc4/WNLpkiZLmirpXEnDa9T1k/mcz0uaY72cfJ1R2N6V9F9JY6rkPUTSDZKm5bwj2nwjzczMrFXd2pUjaQPSxGdTgW8DOwAnAasAy5Wy30ya+n3jnOe7wGmF/QcCvwZOBrYHHgaukLR2qZx/5DK+Q5o/ZG3S5GfVVFYlXgbYoEaeqble6wLbATcCf5X0tVK+75FanG6uUY6ZmZl1UHd35XwfeBTYNWZPkHI9cGKVFoo3I+Ku/Px2SQsAv5W0KCk4OAj4Q0QclfNcK2lV4FBgWwBJ6wKbAxtFxK057SXgbkmbRUR52vlRwF3AZ/Lz26pcw6xCvQBulLQeKcj6eyF92Yj4UNK2wFdbf1vMzMysPbp78OsQ4PWoMmtbtbSS+/LjCGAF0jTy15fyXAd8RdLc+fVWwGuVoCSf5x7g2bzvI5JWIwUkp5NWKN5VUnsDs7eAQaXr+bCdx5qZmVk7dSgwkTSwvAHFlpD7gU3y+IvlO1iXEfnxVWDe/HxGKc8MYG6gUvbKpPVqyh7N+4pGATOBi4HzgEWAzapVpHB9C0v6OrARcGm7r8TMzMw6pSOByXDSH/bytnchz9GkMSa/AZ6W9LKkv0j6VJXylP/4zyPpS8DBQAvwIvAMaY2b8niSL+THYflxKDClStmT876iPYAbImIScG3OM6qN65wKnA2cHBFnVclrZmZm3agjY0ymUr2F4VBgCYCImCZpU2Ad0jiQDUmDUr8uacOIuL9w3E6kP/4V/wa+lbt8pko6DzhY0v+A/wB7Fs7foW4USV8gdQ/9JtdzhqRLSN0580bE+zWucx7SqsS/kfRmRBzWkfOW6jAGGAMwdNGl28htZmbWnDoSmMyKiJZyoqRJ5MAEPhpLcmfekLQmcCtwCLBj4dCbgAOAWcBzETG5VPRPgQtyPoAXgMOBsaTuHkitHotWqevQvK+i0o0zXtKQnHYV6c6hbUjdO7Wu89+5y+pISX+KiDernK9NETEOGAew7IpreOVEMzOzKnp85teIeJA0iLU85mNyRLRExINVghIiYmJEfJl0a+/qpHEl7wCvRsSEnO2xKuVCYeyJpLmA3UiDV58jBSyTgUty3mrdOWWPksa2rNCOvGZmZtZJ3T2PyWJV0kT6g96pqU4j4sWIeJjUuvMtPj7XydXAJ/L8KZXzjSQFMVfnpI2AJUmtM5uUtrOAbSQt1EY1Vs+PL3TmGszMzKx9unsek7/lFoqLgadJXSrfBNYAdu1IQZL2IrVyPAMsC+wLfAAcWckTEXdKug44S9LPSWNPjgJuL8xhMoo0QPbEiJheOsc00uDdHUlBCsBASevk53OTxpj8CrgsIl4tHLsRqRtprZy0laSJwCMR8UhHrtXMzMyS7g5MTiHNvvpr0riTKaQZW7eIiOs6WNZcpFaO5UgDUv8J/DIi3i7l2x04ntSSMhdwJfBjAEmDgJ2BC8tBCUBE3C/pUVLwUglMBpPHx5DGpTwH/IU0vqXoMFJrTMUphfSx7b9MMzMzq1Db855Zd1t2xTXigKM7GqeZWV/kRfzM2kfSfRExsq18Xl24DhYbMshfZmZmZlX0+F05ZmZmZu3lwMTMzMz6DAcmZmZm1md4jEkdTJk8i0sueqPe1TCzDthpl0XqXQWzptCnW0wkjZVU9S+4pDMktRRebyDpekkTJb0j6cmcZ+lCnvGSIm+zJD0t6XhJC5fK/mTO83yeIK7a+feQdL+ktyW9JOksSUt217WbmZk1oz4dmLRXnvl1PGm+k28DOwAnAauQ5kEpuhlYF9g45/kuH59NFtJKxJCmw9+gtA9JXwXOA+4AtifNt7IhcFWeYM7MzMw6oVG6cr5PWs9m15g9Mcv1wIlVWjzejIi78vPbJS0A/FbSohExMaePAu4CPpOf31Yq42vA/RHxo0pCnkX2MuDTuS5mZmbWQY3y3/0Q4PWoMltctbSS+/LjCABJq5ECktOBy4Fd8+rCRYNIrTNFU/Jj1a4fMzMza1u/CEwkDSxvfDwAuB/YRNIhkpbvYPEj8mNlHZxRpKnoLyZ11ywCbFY65jTgS5L2lrSwpE+Rpqy/yevkmJmZdV5/CEyGkwKF8rZ3Ic/RpDEmvwGelvSypL/kgKFMObiZR9KXgIOBFuDFvH8P4IaImARcC0wmBSsfiYirSGsCjSO1nDwODCCty2NmZmad1B8Ck6nA2lW2KysZImIasCmwHvA70srG3wHul/T5Unk7kQKb94FbgQnAnhERkr4ArACcn8udAVwC7CBp3koBkjYhLex3IrAJKZgZBlwqaUC1i5A0RlKLpJap0yZ1+s0wMzNrZP1h8OusiGgpJ0qaRFrBGPhoLMmdeUPSmqTA4xBgx8KhN5HuopkFPBcRkwv7Kt044yUNyWlXke702YbUvQNwLHB5RBxQqM+DwGOku3QuKdc3IsaRWlhYcYU1vXKimZlZFf0hMOmUiHhQ0vXAqqVdk2sEOnMBu5EGtj5XpchRzA5MViaNPyme73FJ75FaXMzMzKwTGiIwkbRYRLxeShMpSHitncVsBCxJak25p7Tvm8BukhaKiLdIgcvHuogkrQLMR+oaMjMzs05oiMAE+Ftu8biYNL5kKCmYWAPYtZ1ljCLd8ntiREwv7shzlOxN6hI6izS+5HhJLwNXA4sDvyYFJf/q4rWYmZk1rUYJTE4h3SXza9K4kynAw8AWEXFdWwdLGkS6o+bCclACEBH3S3qUFLycBfwRmEGa2G2ffL7bgYMi4p2uX46ZmVlzUtvzj1l3W3GFNeMPR91Q72qYWQd4ET+zrpF0X0SMbCtfo7SY9CtDhg70l5yZmVkV/WEeEzMzM2sSDkzMzMysz3BgYmZmZn2Gx5jUwVtvzuLmcyfWuxpm1gGb7Llovatg1hT6VYuJpPGSLqqxr0XSGfn5GZKisL0l6V5JO5WO2biUr7j9LecZIOkASbdJmpS36yStXSprRD5u2x66fDMzs4bXyC0mj5EmWQNYmDTPyYWSNoqI20t59wSeKaVVZpKdDzgQOB04EgjgR8DtktaLiPt6oO5mZmZNqZEDk3ci4q7KC0k3kFYC/ippMrSihyLifzXKeQ9YvrjYn6QbgSdIAco3axxnZmZmHdSvunK6IiI+BN4lLdLXkeM+KK1ATETMIM0su2T31dDMzMwaucUESZXrWxj4FjACuKxK1gGFvBUfRI1pcSXNQ1rEr+p4FzMzM+ucRg5M1gJmFl5/CPwiIsZXyftglbRvAmfUKPtgYBhwUuerZ2ZmZmWNHJg8SloRGGB+4EvAEZImRcQZpbx7kFYlLnq2WqGStiEFJvtFxOPtrYykMcAYgMWHL93ew8zMzJpKfwtMZgEDauwbkPdXvBsRLYXXt0paHDha0pmlbpqHWxn8+pF8i/AFwF8i4oSOVDwixgHjAD69/JpeOdHMzKyK/jb4dSLwiRr7lmD2Lb61PAoskrcOkfQp4CrgRuDHHT3ezMzM2tbfApPbgLUkLVVMlPRFYPG8vzWrk27/ndSRk0paAriW1N0zKiI+6MjxZmZm1j79rSvnLOBnpG6Zw4HngFWAQ4E7SMFDxQKS1snP5yONMfkucEq+dbjos5IWLKVNjYhHJc0HXA0MJc1b8llJlTzTI+KB7rk0MzMz61eBSUS8LWlD4HfA70l3xrxGGvdxcCngWBm4Mz9/nzSY9dfAcVWKPrdK2o3AZqSWmDVy2pWlPM+RbkE2MzOzbtCvAhOAiHiZNL18a3lGt5Un5xsPqI08E9rK05F8ZmZmVlu/C0wawULDBnqlUjMzsyr62+BXMzMza2AOTMzMzKzPcGBiZmZmfYbHmNTBu2/M4oG/tTUXnJn1JZ/7zmL1roJZU2iIFhNJO0u6SdIUSdMlPSHpOElL5v1R2N6T9LykSyRtV6WssZLeKKXNJelcSe9L2kLSAEkHSLpN0qS8XZenrDczM7NO6veBiaRjgX8AzwB7AZsDxwObAicXsh4LrJv3HwjMAC6TdFob5Qv4P2BXYJeIuJY0YduBwL35nF8nrWR8u6S1uu3izMzMmky/7srJLR4/A74dEcUA4xZJ40hBSMWEiLir8Prvkq4DTpV0S0ScWeM0J5FWKd4jIioTrL0HLB8Rkwt1uRF4gjQ77De7dGFmZmZNqr+3mOwL3F8KSgCIiA8i4urWDs7H3Q18v9p+SccB+wB7R8TFpbInF/NGxAzgYWDJDl+FmZmZAf04MJE0CFgPuKaLRV1PWhhwUKn8I4CfAt+JiPPaUZ95gM+TWk3MzMysE/ptYAIMB+YBnu9iOS+SurSGlcr+JXBCRJzeznIOzmWc1MX6mJmZNa3+HJhURBePr7a+zTRSF8+3Ja3ZZgHSNqTA5ICIeLxGnjGSWiS1TH5rUlfqa2Zm1rD6c2AyCZgOLNvFcpYi3VHzZiFtJrAN8DJwtaTlax2cbxG+APhLRJxQK19EjIuIkRExcuhCw7tYZTMzs8bUbwOTiJgJ/BvYootFbQ7cl8srlj8pl/0BcK2kOWZXkvQp4CrgRuDHXayHmZlZ0+u3gUl2AjBS0jfKO/KkaFu2drCkbwFfAP5cbX9EPE8KToaTWk4WKhy7BHAt8DQwKiI+6OxFmJmZWdKv5zGJiCvyLb2nSlofuAx4G1iZdJvvBGbftTNC0jrAIGBpYHtgN+C0iDirlXM8LGlb4AbgUklbAwOAq4GhpHlLPpvmYQNgekQ80K0XamZm1iT6dWACEBH7SbqDFCD8nTQr6wTgcuCYQtb98jYdmEiatXX7iLiiHee4Q9JuwKXA2cBBwBp595Wl7M8BIzp5OWZmZk2t3wcmAHnys4tb2V/tzptaeccCY6ukX0lqbalod5lmZmbWPg0RmPQ38y8y0CuVmpmZVdHfB7+amZlZA3FgYmZmZn2GAxMzMzPrMzzGpA5mvDaTCSe8Wu9qmFkHjPjpJ+pdBbOm0JQtJpLGSnojPx8tKSQtWCXfjyRF4fXGOe+kcv5yXjMzM+u4pgxMusEw4Pv1roSZmVmjcWDSOeOBn0mat94VMTMzayQOTDrnD6RWk+/UuyJmZmaNxIFJ57wAnAX8QtKgtjKbmZlZ+zgw6bzfA0sCe9e7ImZmZo3CgUknRcTTwPnAgZIGtJVf0hhJLZJaJr0zqecraGZm1g85MIFZ+bFacDEA+KCVY38HrADs3tZJImJcRIyMiJHDFxje8VqamZk1AQcmMDE/Vps9aQng9VoHRsQjwKXAL/Fqw2ZmZl3mwATuAaYD2xcTJc0FbAvc1sbxRwCrATv2SO3MzMyaSDNPSR8AETFZ0gnA4ZIGA7cACwP7ACsCX2+1kIj7JV0NbNWz1TUzM2t8zdpiMh8wo/D6IOAXpFaPK4DTSGNLNoqIB9tR3uHdXUEzM7Nm1KwtJisAz1deREQAJ+StpogYT5WxJBFxR7V0MzMz65imajGRtJqkH5HGjvyzztUxMzOzkmZrMTmJ1FpyInBcvSox9+KDvIS6mZlZFU0VmETEJvWug5mZmdXWVF05ZmZm1rc1VYtJXzHztem8esxT9a6GmVXxiZ+vWO8qmDW1TrWYSBor6Y0a+86Q1JKfbywpJK1e2L+IpJMkPSPpfUkvS7pW0g55/4R8TGvb6Jz3EEk3SJqW00e0Vp8q+8ZLuqjweiNJN0t6XdL0XMdjJS1c4/jK9d3e7jfPzMzMaurVFhNJg4CbgflJM6Y+DSwNbA5sSrpTZkdgnsJh1wAXAX8rpD2dH78HPJXL/Go3VHEY8ABwCmmq+tWAw4BPk+7kKRuVH9eTtGxEPF8lj5mZmbVTb3flbAysDnwhIu4tpJ8jSQAR8UDxAEmzgBcj4q4q5S0bER9K2pZuCEwi4lLS2jcV4yXNAMZJGhYRbxbqNQjYBbgJ+DKwB/CHrtbBzMysmfX24Nch+fHV8o48yVmHRMSHXa1QO0zKj3OX0jcntbAcBdzJ7NYTMzMz66QuBSaSBpY3Wp8B9UHgQ+A0SRvk/L2iI3WVNEDSPJLWBH4FXBIR5WBqFKm750bgPGBNSSv34CWYmZk1vK4EJsOBmVW2vWsdEBFPAvsDG5FW7X1L0jWSdu1CPdpjrRp13bBG/oeB90njTV4H9irulDQfaTXiiyLiA+AfpLV13GpiZmbWBV0JTKYCa1fZrmztoIg4Dvgk8EPSgnlfBP4h6cgu1KUtj9ao6/018u8MrE9aYfgzwIWVMTDZdsCCwPkAEfEaMJ5WAhNJYyS1SGqZ9PabtbKZmZk1ta50pcyKiDluw5U0CViitQMj4iXSnS+nSFqAdNfN/pKOiYhJrR3bSe/WqOtbNer3cH56h6RHgVuATUgDXSEFIK8B/5U0JKddAZwgaa2IuK9KmeOAcQBrLPOZDo+nMTMzawZ1n/k1It4hBSkDgL44s1GlVWV5AEmDga2AxYE3gcl5OyHnc3eOmZlZJ/X2PCbDgKl5XEbRSvnxtd6sTzutnx+fzY87keZZ+QZQnrfkIGB3Sft35i4jMzOzZtfb85h8GThS0unAvaQ7dNYDDgSujIgJHSlM0kbAoqTBrQBbSZoIPBIRj3S0cpLOBp4g3T30LvB54Bek24FvztlGAY9FxFlVjh8GXAx8Cbi1o+c3MzNrdr0dmNwNXAbsRvqDPwCYABwOnNiJ8g4j3eFTcUohfWwnyrsHGA38PNftWeCPwPF5IrfFScHVr2scfxUwhRS8ODAxMzPrILnHofetscxn4tqfXNp2RjPrdV7Ez6xnSLovIka2la/ug1/NzMzMKnq7K8eAQYvP4//KzMzMqnCLiZmZmfUZDkzMzMysz3BXTh3MfO1dXjthjslhzawPWPyna7Wdycx6TKdaTCSNlfRGjX1nSGrJzzeWFJJWL+xfRNJJkp6R9L6klyVdK2mHvH9CPqa1bXTOe4ikGyRNy+kjWqtPlX3jJV1UeL2RpJslvS5peq7jsZIWLh1XruNESf+StEaH30wzMzP7SG/P/DqINFHZ/MARwNPA0sDmwKbAP4EdSTOrVlxDWkvnb4W0p/Pj94Cncplf7YYqDiOtKHwKMBFYjTQnyqeBbUt5/w78KT9fkjTr67WSVomIyd1QFzMzs6bT2105GwOrA1+IiHsL6edUVu+NiAeKB0iaBbwYEXdVKW/ZPPHZtnRDYBIRlwLFCUbGS5oBjJM0LCKKywK/UqyTpMeAh4F1gX91tS5mZmbNqLcHvw7Jj6+Wd3RmbZmI+LCrFWqHymrHc7eRr7JS8aAerIuZmVlD61JgImlgeQPUyiEPktbHOU3SBjl/r+hIXSUNkDSPpDWBXwGXREQ5mFKhrGWAP5BWG76lJ6/DzMyskXUlMBkOzKyy7V3rgIh4EtiftL7NbcBbkq6RtGsX6tEea9Wo64Y18j8MvE8ab/I6sFeVPD8rlPM8sCWwS0RM6c6Km5mZNZOutFhMBTarkn4osEStgyLiOEkXANuTxpx8BdhC0u8j4qAu1Kc1j1I9YPprjfw7A4OBz5AW7LtQ0ral7qZzmL3w4DDga8ClkjaMiIe6p9pmZmbNpSuByayImOM2XEmTaCUwAYiIl0h3vpwiaQHSXTf7SzomIia1dmwnvVujrm9VyxwRD+end0h6lNQ9swlwUyHba8UyJV0PfI4UyOxS5VxjgDEASw/9RCcvw8zMrLHVfebXiHiHFKQMAPriAjL358flW8uUW1MeA1apsX9cRIyMiJHDFhjazVU0MzNrDL0amEgaJmlAlV0r5cfXerM+7bR+fny2tUz5dudVgRd6vEZmZmYNqrfnMfkycKSk04F7SXforAccCFwZERM6UpikjYBFSYNbAbaSNBF4JCIe6WjlJJ0NPEG6e+hd4PPAL4A7SZO4FS0haZ38fChpjMnqwCEdPa+ZmZklvR2Y3A1cBuxG+oM/AJgAHM7sgaQdcRjpDp+KUwrpYztR3j3AaODnuW7PAn8Ejq8yZ8rX8gZpIPBjpLty/tmJ85qZmRmgTsxrZl20xjKrxnX7nV3vaphZFV7Ez6xnSLovIka2la/ug1/NzMzMKnq7K8eAQYvP7//KzMzMqnCLiZmZmfUZDkzMzMysz3BXTh3MfP0tXvvj+HpXw8yqWPzHG9e7CmZNzS0mZmZm1md0KjCRNFbSGzX2nSGpJT/fWFJIWr2wfxFJJ0l6RtL7kl6WdK2kHfL+CfmY1rbROe8hkm6QNC2nj2itPlX2jZd0UeH1RpJulvS6pOm5jsdKWrjKsQtL+o2kRyS9J+ktSbdJ+k6N2W3NzMysDb3alSNpEGkG1fmBI4CngaWBzYFNgX8COwLzFA67hrTI398KaU/nx+8BT+Uyv9oNVRwGPECaqG0isBppsrZPA9sWrmMxYDwwBDgOuC/X+cv59UTSRHJmZmbWAb09xmRj0rTtX4iIewvp5+S1ZoiIB4oHSJoFvBgRd1Upb9mI+FDStnRDYBIRlwKXFpLGS5oBjJM0LCLezOl/Jk1DPzKvlFxxjaSTgMFdrYuZmVkz6u3AZEh+fLW8IzoxBW2VaeJ7wqT8ODdA7i7aEfhJKSip1On5XqiTmZlZQ+rS4FdJA8sboFYOeZC0cN9pkjbI+XtFR+oqaYCkeSStCfwKuCQiKsHUl/Jx1/RKxc3MzJpIVwKT4cDMKtvetQ6IiCeB/UkL790GvCXpGkm7dqEe7bFWjbpuWCP/w8D7pPEmrwN7FfYtlR/dMmJmZtbNutJiMRXYrEr6ocAStQ6KiOMkXQBsTxpz8hVgC0m/j4iDulCf1jxK9YDprzXy70waJ/IZ4NfAhZK2LXU3dajrSdIYYAzA0kMX78ihZmZmTaMrgcmsiJjjNlxJk2glMAHIYzNOAU6RtADprpv9JR0TEZNaO7aT3q1R17dq1O/h/PQOSY8CtwCbADcBlXEly5LuCGqXiBgHjANYY9lPe0lnMzOzKuo+wVpEvEMKUgYAK9a5OtXcnx+Xz4+3klpLtqhPdczMzBpXrwYmkobVmHxspfz4Wm/Wp53Wz4/PAkTEc6Rbin8paY6WIUnLSPpML9bPzMysYfT27cJfBo6UdDpwL+kOnfWAA4ErI2JCRwqTtBGwKGlwK8BWkiYCj0TEIx2tnKSzgSdIdw+9C3we+AVwJ2kSt4rvk7p3WiQVJ1jbCPghaTzLfzt6fjMzs2bX24HJ3aQZUXcj/cEfAEwADgdO7ER5h5GCgYpTCuljO1HePcBo4Oe5bs8CfwSOL86ZEhGvS1on5/suaRbbmaS7ePYFruzEuc3MzJqeOjGvmXXRGst+Oq77ea0bgsysnry6sFnPkHRfRIxsK19vt5gYMGixhfzlZ2ZmVkXd78oxMzMzq3BgYmZmZn2Gu3LqYNbrU3n95CvqXQ0zq2KxH25X7yqYNTW3mJiZmVmf0a2BiaSxkt6ose8MSS35+caSQtLqhf2LSDpJ0jOS3pf0sqRrJe2Q90/Ix7S2jc55D5F0g6RpOX1Ea/Wpsm+8pIsKrzeSdLOk1yVNz3U8VtLCpeMmSDqmw2+cmZmZAX2kK0fSINIEZvOT5gR5Glga2BzYFPgnsCNpErOKa0hr7PytkPZ0fvweaR2bm4GvdkMVh5HmKDkFmAisRpor5dPAtt1QvpmZmdFHAhPSKsOrA1+IiHsL6edIEkBEPFA8QNIs4MWIuKtKectGxIeStqUbApOIuJQ0DX3FeEkzgHGShkXEm109h5mZmfWdMSZD8uOr5R3RiRngirO09qDKKshz98K5zMzMmkKPBCaSBpY3QK0c8iBp3ZzTJG2Q8/eKjtRV0gBJ80haE/gVcElEzBFMmZmZWef0RGAynLRuTHnbu9YBEfEksD9p3ZvbgLckXSNp1x6oX9FaNeq6YY38DwPvk8abvA7s1cP1MzMzayo90TIxFdisSvqhwBK1DoqI4yRdAGxPGnPyFWALSb+PiIN6oJ4Aj1I9YKq1kM3OwGDgM8CvgQslbdue7iZJY4AxAEsPXbRztTUzM2twPRGYzIqIOW7DlTSJVgITgIh4iXTnyymSFiDddbO/pGMiYlJrx3bSuzXq+laN+j2cn94h6VHgFmAT4Ka2ThQR44BxAGsuu5JXTjQzM6uirwx+nUNEvEMKUgYAK9a5OtXcnx+Xr2stzMzMGkifCEwkDZM0oMqulfLja71Zn3ZaPz8+W9damJmZNZC+Mo/Jl4EjJZ0O3Eu6Q2c94EDgyoiY0JHCJG0ELEoa3AqwlaSJwCMR8UhHKyfpbOAJ0t1D7wKfB34B3EmaxM3MzMy6QV8JTO4GLgN2I/3BHwBMAA4HTuxEeYeR7vCpOKWQPrYT5d0DjAZ+nuv2LPBH4PhemjPFzMysKagT85dZF6257Epx3QHH1bsaZlaFVxc26xmS7ouIkW3l6ystJk1l4GKD/eVnZmZWRZ8Y/GpmZmYG7sqpizxPyuP1rkcvWgR4o96V6CXNdK3QXNfbTNcKzXW9zXStUL/rXS4i2pxh1F059fF4e/rZGoWklma53ma6Vmiu622ma4Xmut5mulbo+9frrhwzMzPrMxyYmJmZWZ/hwKQ+xtW7Ar2sma63ma4Vmut6m+laobmut5muFfr49Xrwq5mZmfUZbjExMzOzPsOBSS+TtKWkxyU9JenAetenO0laRtLNkh6R9LCkn+T0YZKul/Rkfhxa77p2F0kDJD0g6cr8+pOS7s6f7wWS5q53HbuLpCGSLpL0mKRHJa3b4J/tvvnn+H+SzpM0b6N8vpJOk/S6pP8V0qp+lkr+mK/5IUmfr1/NO6fG9R6df5YfknSppCGFfQfl631c0hZ1qXQnVbvWwr79JIWkRfLrPvnZOjDpRXkF5ZOBrYBVgVGSVq1vrbrVLGC/iFgVWAf4Yb6+A4EbI2Il4Mb8ulH8BHi08Poo0hpKKwKTgW/XpVY940TgmohYGViDdN0N+dlKWgr4MTAyIlYnrZG1B43z+Z4BbFlKq/VZbkVa6X0lYAzw516qY3c6gzmv93pg9Yj4LGmR1oMA8nfWHsBq+ZhT8nd3f3EGc14rkpYBNgeeLyT3yc/WgUnv+gLwVEQ8ExEzgPOB7etcp24TEa9ExP35+VukP1xLka7xzJztTGCHulSwm0laGtgG+Ft+LdJK2RflLI10rYOBDYFTASJiRkRMoUE/22wgMJ+kgcD8wCs0yOcbEbcCb5aSa32W2wNnRXIXMETSEr1S0W5S7Xoj4rqImJVf3gUsnZ9vD5wfEdMj4lngKdJ3d79Q47MFOJ60SG5xYGmf/GwdmPSupYAXCq9fzGkNR9II4HOklaMXj4hX8q5XgcXrVa9udgLpF72ywvRwYErhy66RPt9PAhOB03PX1d8kLUCDfrYR8RJwDOm/y1eAqcB9NO7nC7U/y2b43voWcHV+3nDXK2l74KWI+E9pV5+8Vgcm1u0kLQhcDPw0IqYV90W6Dazf3womaVvg9Yi4r9516SUDgc8Df46IzwHvUOq2aZTPFiCPr9ieFJAtCSxAlebxRtVIn2VbJB1M6oY+t9516QmS5gd+Cfy63nVpLwcmveslYJnC66VzWsOQNIgUlJwbEZfk5NcqzYP58fV61a8brQ98VdIEUpfcl0ljMIbkpn9orM/3ReDFiLg7v76IFKg04mcLsBnwbERMjIiZwCWkz7xRP1+o/Vk27PeWpNHAtsCeMXvujEa73hVIAfZ/8vfV0sD9kj5BH71WBya9615gpTyyf27SAKvL61ynbpPHWJwKPBoRxxV2XQ58Iz//BnBZb9etu0XEQRGxdESMIH2ON0XEnsDNwC45W0NcK0BEvAq8IOnTOWlT4BEa8LPNngfWkTR//rmuXG9Dfr5Zrc/ycmDvfAfHOsDUQpdPvyVpS1JX7Fcj4t3CrsuBPSTNI+mTpIGh99Sjjt0hIv4bEYtFxIj8ffUi8Pn8O903P9uI8NaLG7A1aQT408DB9a5PN1/bBqTm34eAB/O2NWnsxY3Ak8ANwLB617Wbr3tj4Mr8fHnSl9hTwIXAPPWuXzde55pAS/58/wkMbeTPFjgMeAz4H3A2ME+jfL7AeaSxMzNJf6i+XeuzBES6m/Bp4L+kO5Xqfg3dcL1PkcZXVL6r/lLIf3C+3seBrepd/65ea2n/BGCRvvzZeuZXMzMz6zPclWNmZmZ9hgMTMzMz6zMcmJiZmVmf4cDEzMzM+gwHJmZmZtZnODBpcpJG5pVE38irTj5Y7zqZWfeSdEb+/R7RxXJG53JG9/a5+wpJwyW9KemUetelP5B0uaSnO7IStwOTOpM0n6T3JR1XSBsnaVphhsmeOvfCwFWkBarOJ83b8Jc2jhmRv2Qm1Nj/BUkTJX0gaZ/urnN/I2luSd+WdJWkVyRNl/SWpAclnSDps/WuY18kaRNJZ0p6Ir9fMyS9KulGSQfmBRT7NUkT8u/SW5KqrjEkaXzOs2Jv189qOgyYDzi8mChpbP6sQtIPqh1YCOwOr7a/swrfy21tXyodt4ukP0m6Lf/NCUnntHKeQZJ+Iun0/B02Ix/znVaq92vSzLM/bu/19OgfPmuX9UkTN91USNsUuDVmLxbWU74ALEaa6O13XS0sz6R4EWmJ+F1j9pT0TUnSp0gTka0CvEFaZv15YG5gVWAf4MeSdoiIhpkBuCtysFxZ2XYmcCvwL9LaPIuSfmaPBA6TtE5EPFCnqnanBUl/7HoykD8I+D19YLrx/kzSssD3gNMj4uVWsh4q6exIq6z3himkn6FqliEtUjiJOWew/RWwBvA2aTK2lds4zwKkxUsBXiMt9rhMzdxARDwo6RrgYEmnxMdn2a3KgUn9fRn4gPQFXFmVd3nSbHw9bcn82NovWLtI2gs4jfQHZOtIS283rfwf8I2ktSdOAH4ZEe+V8iwGHEqaQbXpSRpAWmdpM+AWYK+IeKFKvlWB3wAL924Ne8xTwHck/f/2zjzequq649/FDDHlKc6aQA2ixiSi+dCKQ3kGh0pDSQyl0aQJWo0So2AliUZtXmLURI2xmqKlRh9qEgdSp4jFFvOIqJgSRY3BIuIzlA+ggAwGHwRc/WOtk3vevufcc+4bgJb9+3zO59y79trztPbea6/zT6q6qDsiUDMzvuNNjf/fx7nYvNlcg2cJMBT7yOVl2yFNqOo6oCnLTUSu8Z93qurmwPkiTCBZAozCPrlQC5swa94LVXWFiDRhY1gRZgCnAmcAtxVy72jTs7vaA7wfa7TJswAzBZz8/xpm1v3TKVr/OsIfDfw7sBbYjJm//y4wMMUzxOPIeiYWhJ/4bU3RpgLvYauxj9bw0+y/78F2ENo8/5/Miasv1rlfwjrEBuBJYELAtxuwBXgqoPf3OBSb5NJuk5x+VorW4rRe2Nc4X/UyXAZ8D+hTRz38q4f1kxK8fVO/h3l9LQDe8vjfAKYDB2b4bfR4mjCT8Y9iq6dN2OR+TE6cPbEV+lPAeuBdbHC6DTg44O0FfBmY73WwCXge+ArQo0ZdDwPuxT4G9x7QWFAOX3C/i4H3lSi3Xhm0A4EfAku97NZg3wMZkcHb5PE1Yt+/+ZXnba230QNy4t0D27VZ5OW2HhNCT65zLGj1+Mf7+5EMnqRNDs1wO9TLeRnW/lcBPwEOyeBt9nCGBHQBJmPfAWrD+vAPgYGevtaAf6KHMxE4wdO30dvFo8BhNeI+CPgHzMx/GzYh/gD4k5zy+TgmqL5JpR9MA/YriOMC7LMJ7wItqXx+EXga61dtXm6zgb8tWV+CLeJ+l+OetKcLvRw3EfTZVPl9p5620tEH6I0JpAocWsDb6Hx31xF+kuezC/j6eX08Uyrc7VE48WlXQUnDrOdpLBn2udgEsBHbvfguNpko8DLQ4HwN3qAedLcH/X8TMLwgjiHup9U76vX+/xVgcIGfX/ggM98HpBk+QGwDTgj89KEyKC8CrsN2kVY57eqAfx629f/+FO3EVBk2B/wznT44RUviu8878+3Ybsdip99Rsh7SAlHVJFHg9xJMsHgAuMnL9zGv1xUEkyWVweTn2EA4x/3c5+X6bpgGL9vH3d/vgFswwetebCKfmOLtjQm6SR3f6mXygtPuyqnrJ4G3gWe9rm/FPhxWK+9Put9zOti3jsIE3ve8zK7HJqx12MQ2JuBvStV3m7+vw3Yvk3bXN/AzGHjd3X/peZuOTVjv1ZN2KoJJL0yIVKr7QdImhwb0v/T6Tr58fC0mlLRhgtJRAX8z2YLJNKcvT7W3xZiQtpx8wWSmx/2wl9mjTn8T/w5LRtwPeZv4F29vC52+AOgX+Pmk19kWz9c1VNrscuBPc+J4xOv7x9j4d5W7X+3uS7Fx5GrgDuw7SDNL1tdHPIyf5rgn7els7OhEgRk55be9BJPPeHxzS/A20k2CifPOA7aSWiTn8m6PwolPu8oZjK2QxgM3eKVekaL9HtM3GZ969ioZ7mZs5XJo4JYMPtMDetJJJtaR/iHu53+wD5spJmgMKuFHgW8Gbqc4fVZAvzShk1oZYzoxre52TIr+baf9VYp2jXeEOcCyFL0HNgG/FsTZ4mH8mtTH6LBz1SXYRL9viTI6PimjDrSPA8j4MBxwssd/S0BPBpOqesQEVQWmBfRkkH44jAvbpdor9T8ZeG4GeqboPbEvSSswLqeury6TZ/fXC5uEFPhQB8qtl9dRGzAqcNsfm8xW0H53KsnbBoKdPmwyVKp351owAeSzAb0Bm2jfBfYpmeakHfcCRni4C8C+YRa0yaEp2u7YBL8a+HAQ5kcwfYHnAnozgWCSaqf/jS9anN6HinDWGoQz0elbgdGB2zXu9rWcuFfTfiHQA9sRUeCKFH03rH9uA44Pwvq68z+eE0eV0OLua7Axa0CG254hLae+zvM4Ls5xT9rT2Z63Fz0PwzPK7zuB3+FUFodln4YSaZ7t8Z1RgreR7hVMfuC8Ywp5yyYgPl3/AN/HBuP3+f9hXnGTOhDWZeRMBthAtgEbNNMDc9JJJtYRzxDa7+asoUACpv0uS88M9zeA1QHtVWygrtp+xL4MqsDtKdoop92Qov0KW7Gf727DnH4U2YJai9NPzIjzW+6WeewU8E5w3vld3F5eBJYGtGQwmZfB3xtb1S5I0XpSOerZvyC+RIBbQfaxSYPX0X0Zdb2SOr68iwmcSZvql+HeSPXA/KmU+zj3e11O+JMJBkUqg2rV6hU7plDg+hTtCKfdnxNHkoYvl8xzq/P38v8/9f+fz2iTQzPycn5OuMkE8OEUrZlqweQ2p30hI4xjqS2YVE1e2M0LJdiBSMV9RYafg7DJ+/UU7XPOX3UMiglxr7v7BzPimJxTJmvcX4e/Bk1FoM+c5AkmaSqLrv/IKL9QMEno9TxDCtI7BOufq8vkm+4XTBKh8rwi3qj8umPxCeC/VPX3/n+Uv+d2IKyj/P1E6KCqb4vI88BfYOfSL3Qg/BBvYSuQI4E7RWSCVitWhVioqtsy6MuAkckfEUn0cJar6isZ/Ekej0zRnsEEr9EexkCsTK5N8Y/Gtqk/EYQTYkFOGqGbFVVFRLCBeSI2Ee6OCRMJtuR4rUqzqv5BRFbRPs2HYvoDz2rtWwVggvIemJB4uSWtCu9it45CvFCiPdSDRqqV7GZgx5BQaT+DXSEvxMH+PgzbhUujbH0ncQzMiWOvVBwdwaWYbtlVIjJTVdty+JJ0HJGTjmGpdPy2RnxJ/5mX4TYf2xXJQ0f6SNW4pqpLRWQZMEREGtSUOGuNZVtF5JfYpHskdhSZRnjrJMGPMd2T34rIfZ6WZ1R1fQ5/Fgb5++0yzKo6W0QeB04WkTGqGra7NG8ztRVqO4JzsOP2GV3cFzuKtf7es4gxCibbESLSiA2wYKvRI4AFqcFlDLZ6mJBMAqraRDkM9Hee5n1CbygZXhE2YRP8Y8BfAw+JyKc1uHkSYF0OfSvtberUnRdV3SIi84ATRWQv4BhsQp+jqotEZAUmmNzibyVHMPHBMSuN0F5IyEOSvgNK8Ia4AZjiYczGtqaTMp2IHdllYV0OfSvt09zg7zLXRpOB+GBqa97vlkFbWSL8NNZiuzu9saOXpWlH7wdNACJyInb1Oiutf1MQT1Za12XQsuo7ieMkf+qJoxCq2ioiN2PK5JMxPYwsJOk4pyDIonQk/WxVRlq2iciaGn7XZfjZ6uNWXh+pisexEmvXAz3czoxlee3uIqxNnYnpcV0CbBWRWdjRzJIcf2kk/bBfCd4EX8V03a4Vkdl1+OsU3AbWmf53+vaKtwD9/V1rjgCiYLK90Uj1AD/CnzTSPE0lw04k/30xRdcQ+wV8nYaqrhORkzDFt1OAWSIyVlXf6WTQ6bxkIS8vT2ATxmhMMGnDbp0kbqeKSF/sbP1lVX2zk+nMwwJM3+dAERmmqovLePLrwxdiCnnHaGADQURO74K0rfN3GaEpKd8HVPW0OuPRuphtUnsWOA6rv6UFXkIkaR2n3WcTJoljsqre1E1xXIUpTl4qIj8qSMcRqvpiJ+La4O99CMrbr24PomvtnuyD6bOESPr5+uBdb/+HnHbnO7U3Ajd6PzsO+CwmyB4uIoeX2FVIxotBNbnax/uiiMzAhISzMOG7CiIyHLPdUw9uzFlEAYzFymmuqmaV+Y5AUm6F4260/LodoapNqiqqKph+yWbsKrBQ2f6dlPA4vSye93dj6CAiDZhyVRt206DL4ELIqdgKthF43I9ROhPmRuA14AAROTiD5QR/PxfQ5/h7NLab83RqO3wOdiwxCVNmnUM3wXeN7vK//1jE78IS2Hl7D0yxLxRKDnT3zuIVTDj5mIjsX5L3aBHp3QVxFyGxb3CxiAyo0+98fx9fk6tz6PY4fKK5Ets1yNul6qp0JGPGcRluR9P1C9dRIUFEDsIMdLWmJtlaY1kvKvkO+38pqOqbqvpvqjoBW7B8CFMaLkIiBBYZIQtxObbD/G1s7MnCcKy+63kaasT5JX/vLLslUCm3hUWMUTDZcTgBU45MJs5Gf7d0MLy7MWn8ggzz1Vdixqju7o6zRjVLfmOxq3ojgTkiskcng70dOx+9zldvAIjIntgtpoQnjeewVdQ44HDaCx/Jsc2lwf/uwuWYDs7nROQ6EekfMojIniJyE7ZyA1OGBDguyPNumF2UTk8UvnKchm2r3poSipK4+vhRGGqWh2/GVl435eRhPzd41hW4G6uzQ4BHJN/sfEMG7SFMmD1fRMZkeRKRkR0QeP4IVV2AXWk+TUTOyonjo74i7wymYXk5F9OlCHEHJjB+U0T+LCMNPfzYuAh3+vuy9GJC7JsmnbYEnYHJIvLHo0gR6YFdNe6B5SnBg9jR3ukicnQQxhRMyfY/VTXUL8mEiPQVkWMz6L2xxQqY4FCEJ7Gj9jBNNeG6XN/HdoCm5PA0pxekJZ/WrLC8jE/GFH5/Vk9auxlHY4q4vylijEc5OwCpHYwrU+RGYGWOsmch/Hx6CnZH/zlX8HoLW6WMxFa/X+9woovj3ywin8GuWY4HfiEiJ3XiuOR6bCdmHPCCnwUPwLZe9wauVdV2Snt+Lt7ifiAlmKjqGyLyGrY62kbHFIxLQ1VXichobJCdCnxRRNIm6Q/D6rwvvoWrqitF5B5MUFnoinMDseOpNmylMbwLkvct4M8xYXKxiPwcs33zAWxA+yoVRbwrMV2o84CxIvIEtr2/N6Z7cix2I6yWkmUpeP2dhk2Y44ClIjIXG8g2Ycqlh2PHdFuwG1eJ3z+439nAoyLyNFZemzxfI7Adp/0oNwnl4QxMqP2RiFzoaViHGXb7GLbyHkmJ7eo8uL7UpZhdlSqdIlVdIyLjMVs380VkDnZ8q1heR2Lb5jV1IVR1rohMx1bXL4vIz7DFzVhMwE9ss3QVnsLa9b0e/ilY2/o1pqSepOsdF/zuB+aKyP1Yv/k41j5XYkJbWfQH5onIEo/rDaxsTsL64cNawuKuqq73sm4Ukd1VtZQSrONarJy3xzePkuvKhUqvIvIpKkdIydHZSBFp9t+rVXVq4OcSKrsfw/19pogkO2/zVPW2wM8hwAexm5DFx7xF13bi0/UPlWuFjSnaCuCeLgj7ZMwQ0dvYUdESrFM0ZPBOpOPXhVtz3HtiE4tix0b7p/w05/hpsaZYRe+HWWD9DaYwtRG7QXB6jfRd4HGtJ7iajBl2UuxGSul0dLSs3F8f7HrzLK/jLZ6PlzCDVqH9jAGYnkFik2MZJmwOykofKcuvOfG3ZtUVtij5CnaL4R3Mfs6r2NZvaMxLgL/DBL21noflXhffAD6Q0T4y67qOchvt7WiJpy2xbPqEx1llBdf97Y0Z1kqEmXc8XzOBz9PeJk4TQT8skw/MevM3sEnuHW+br2O6Vl+ihNXaVN0oGVex3f1pKldDsyy/DsGstL7qbWUDtgC5i9RVaudtJuOKKTaBXeT+NmPCyD9jAvFG7CZd6X7gbi05cR8EXEzF8utyTO8jz/LrCEz4esvrPzEGWHXNPS9/7tYbs6j9mIfR5mHOxwTueiw6J2N3lUkHCq7OUrErpHSTgTVs/F3ucRQad0ylOe/JGjtaCvxk9ZnkqvXwMvkQ9xQREREREQGA63YtxhZLXaF0/f8CfsT6EiYoHalxAi2EHxcvBRap6oll/EQdk4iIiIhdFCKyr+t6pGkDqHxB9oHtnqidGGo6WlOxI6h6b6rtqpiEHRNdXNZD1DGJiIiI2HUxBVMybcGOGvfFjtEOxI4+7t9hKdtJoaqzRGQy9dkz2ZWxGfh7VS1t2DMe5URERETsonAF7amYEuMemGG5xZgS+42qmml3IyKiOxEFk4iIiIiIiIidBlHHJCIiIiIiImKnQRRMIiIiIiIiInYaRMEkIiIiIiIiYqdBFEwiIiIiIiIidhpEwSQiIiIiIiJip0EUTCIiIiIiIiJ2GvwvEcDLa3WbQXwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_to_plot = 30\n",
    "\n",
    "# get known cancer genes and candidates'\n",
    "nodes = pd.DataFrame(node_names, columns=['ID', 'Name'])\n",
    "known_cancer_genes = []\n",
    "candidate_cancer_genes = []\n",
    "n = 0\n",
    "with open('../data/pancancer/NCG/cancergenes_list.txt', 'r') as f:\n",
    "    for line in f.readlines():\n",
    "        n += 1\n",
    "        if n == 1:\n",
    "            continue\n",
    "        l = line.strip().split('\\t')\n",
    "        if len(l) == 2:\n",
    "            known_cancer_genes.append(l[0])\n",
    "            candidate_cancer_genes.append(l[1])\n",
    "        else:\n",
    "            candidate_cancer_genes.append(l[0])\n",
    "\n",
    "# only consider those that are in the network\n",
    "known_cancer_genes_innet = nodes[nodes.Name.isin(known_cancer_genes)].Name\n",
    "candidate_cancer_genes_innet = nodes[nodes.Name.isin(candidate_cancer_genes)].Name\n",
    "\n",
    "# get interaction partners of known cancer genes\n",
    "cancer_gene_neighbors = {}\n",
    "for cancer_gene in known_cancer_genes_innet:\n",
    "    partners = get_all_neighbors(cancer_gene, adj, node_names)\n",
    "    for partner_id, partner_name in partners:\n",
    "        if partner_name in cancer_gene_neighbors:\n",
    "            cancer_gene_neighbors[partner_name] += 1\n",
    "        else:\n",
    "            cancer_gene_neighbors[partner_name] = 1\n",
    "\n",
    "# plot\n",
    "cancer_gene_neighbors_sorted = sorted(cancer_gene_neighbors.items(), key=operator.itemgetter(1), reverse=True)\n",
    "to_plot = cancer_gene_neighbors_sorted[:n_to_plot]\n",
    "fig = plt.figure(figsize=(8, 10))\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h')\n",
    "plt.title('Top {} Neighbors of Novel Predictions'.format(n_to_plot), size=20)\n",
    "plt.xlabel('# of Known Cancer Gene Neighbors (N={})'.format(len(known_cancer_genes)), size=20)\n",
    "plt.gca().tick_params(axis='y', labelsize=15)\n",
    "fig.savefig(os.path.join(all_models_dir, 'ncgknown_neighbors_consensus.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(155, (158,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "novel_predictions = [i[0] for i in consensus_sorted]\n",
    "A = pd.DataFrame(adj, index=node_names[:, 1], columns=node_names[:, 1])\n",
    "novel_with_known_interactions = A.loc[A.index.isin(known_cancer_genes), A.columns.isin(novel_predictions)].sum(axis=0)\n",
    "(novel_with_known_interactions > 1).sum(), novel_with_known_interactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_to_plot = 30\n",
    "\n",
    "neighbors_sorted = sorted(consensus_neighbors.items(), key=operator.itemgetter(1), reverse=True)\n",
    "to_plot = neighbors_sorted[:n_to_plot]\n",
    "fig = plt.figure(figsize=(8, 10))\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h', color='darkblue')\n",
    "plt.title('Top {} Neighbors of Newly Predicted Cancer Genes Across All Omics & Networks'.format(n_to_plot), size=20)\n",
    "plt.xlabel('# of Prediction Neighbors (N={})'.format(len(consensus_sorted)), size=20)\n",
    "plt.gca().tick_params(axis='y', labelsize=15)\n",
    "fig.savefig(os.path.join(all_models_dir, 'consensus_neighbors_all_networks.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[predictions.Name == 'APP'].label.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = pd.DataFrame(neighbors_sorted, columns=['Name', 'Consensus_Gene_Neighbors']).set_index('Name')\n",
    "A = pd.DataFrame(adj, index=node_names[:, 1], columns=node_names[:, 1])\n",
    "consensus_with_neighbors = x.join(A.sum(axis=0).rename('Degree'))\n",
    "consensus_with_neighbors.Degree = consensus_with_neighbors.Degree / A.shape[0] #probability to have this gene as neighbor\n",
    "consensus_with_neighbors.Consensus_Gene_Neighbors /= len(consensus_sorted)\n",
    "consensus_with_neighbors['Name'] = consensus_with_neighbors.index\n",
    "long = pd.melt(consensus_with_neighbors.head(10), id_vars=['Name'], value_vars=['Consensus_Gene_Neighbors', 'Degree'])\n",
    "fig = plt.figure(figsize=(6, 5))\n",
    "sns.barplot(y=long.Name, x=long.value, hue=long.variable, orient='h')\n",
    "#consensus_with_neighbors.plot(kind='bar', stacked=True)\n",
    "plt.gca().tick_params(axis='y', labelsize=15)\n",
    "plt.gca().tick_params(axis='x', labelsize=15)\n",
    "plt.ylabel('', visible=False)\n",
    "plt.xlabel('Fraction of Interaction', fontsize=20)\n",
    "gene_count = 0\n",
    "for lab in plt.gca().get_yticklabels():\n",
    "    gene_name = lab.get_text()\n",
    "    if predictions[predictions.Name == gene_name].label.any():\n",
    "        plt.gca().get_yticklabels()[gene_count].set_weight('bold')\n",
    "    plt.gca().get_yticklabels()[gene_count].set_fontsize(20)\n",
    "    gene_count += 1\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(all_models_dir, 'consensus_neighbors_all_networks_withrandom.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.DataFrame(adj, index=node_names[:, 1], columns=node_names[:, 1])\n",
    "interactions_with_cancer_genes = A[A.index.isin(known_cancer_genes)].sum(axis=0).rename('Cancer_Gene_Interactions')\n",
    "pred_with_interactions = predictions.join(interactions_with_cancer_genes, on='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman Correlation: 0.6270474815853091\tP-value: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "sns.kdeplot(pred_with_interactions.Prob_pos.rank(), pred_with_interactions.Cancer_Gene_Interactions.rank(), cmap='Reds',\n",
    "            shade=True, shade_lowest=False)\n",
    "\n",
    "#correlation = pred_with_interactions.Prob_pos.rank().corr(pred_with_interactions.Cancer_Gene_Interactions.rank())\n",
    "correlation, pvalue = scipy.stats.pearsonr(pred_with_interactions.Prob_pos.rank(),\n",
    "                                           pred_with_interactions.Cancer_Gene_Interactions.rank()\n",
    "                                          )\n",
    "print (\"Spearman Correlation: {}\\tP-value: {}\".format(correlation, pvalue))\n",
    "#plt.title('Correlation Output Probability & Cancer Gene Interactions(R = {0:.2f})'.format(correlation),\n",
    "#          fontsize=25)\n",
    "plt.xlabel('GCN Output Probability (Ranked)', fontsize=16)\n",
    "plt.ylabel('# Interactions with Cancer Genes (Ranked)', fontsize=16)\n",
    "plt.gca().tick_params(axis='both', labelsize=12)\n",
    "fig.savefig(os.path.join(all_models_dir, 'correlation_cginteractions.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# of Interactions\\nwith Known Cancer Genes')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=pred_with_interactions, x='label', y='Cancer_Gene_Interactions', showfliers=False)\n",
    "\n",
    "plt.gca().tick_params(axis='x', labelsize=15)\n",
    "plt.gca().set_xticklabels(['Other', 'Known Cancer\\nGene'])\n",
    "plt.ylabel('# of Interactions\\nwith Known Cancer Genes', fontsize=15)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do NPCGs have a Higher Node Degree than Other Genes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nodes_with_degrees = pred_with_interactions.join((A.sum(axis=1) // 2).rename('Degree'), on='Name')\n",
    "npcgs = [i[0] for i in consensus_sorted]\n",
    "\n",
    "cg_idx = nodes_with_degrees.drop(['Name', 'Degree', 'label', 'Num_Pos', 'Prob_pos', 'Std_Pred', 'Cancer_Gene_Interactions'], axis=1).any(axis='columns')\n",
    "nodes_with_degrees['Cancer_Gene'] = cg_idx\n",
    "nodes_with_degrees['NPCG'] = False\n",
    "nodes_with_degrees.loc[nodes_with_degrees.Name.isin(npcgs), 'NPCG'] = True\n",
    "\n",
    "nodes_with_degrees['Gene_Set'] = 'None'\n",
    "nodes_with_degrees.loc[nodes_with_degrees.NCG_Candidate_Cancer_Gene, 'Gene_Set'] = 'NCG CCGs'\n",
    "nodes_with_degrees.loc[nodes_with_degrees.NCG_Known_Cancer_Gene, 'Gene_Set'] = 'NCG KCGs'\n",
    "nodes_with_degrees.loc[nodes_with_degrees.OncoKB_Cancer_Gene, 'Gene_Set'] = 'OncoKB Caner Gene'\n",
    "nodes_with_degrees.loc[nodes_with_degrees.Bailey_et_al_Cancer_Gene, 'Gene_Set'] = 'Bailey et al. Cancer Gene'\n",
    "nodes_with_degrees.loc[nodes_with_degrees.ONGene_Oncogene, 'Gene_Set'] = 'ONGene Oncogene'\n",
    "nodes_with_degrees.loc[nodes_with_degrees.NPCG, 'Gene_Set'] = 'NPCG'\n",
    "\n",
    "nodes_with_degrees['KCG_interaction_fraction'] = nodes_with_degrees.Cancer_Gene_Interactions / (nodes_with_degrees.Degree + 1)\n",
    "\n",
    "sns.boxplot(data=nodes_with_degrees, x='Gene_Set', y='Degree', showfliers=False)\n",
    "plt.ylabel('Degree', fontsize=15)\n",
    "_ = plt.setp(plt.gca().get_xticklabels(), rotation=90, fontsize=15)\n",
    "plt.gca().tick_params(axis='y', labelsize=18)\n",
    "plt.xlabel(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=nodes_with_degrees[nodes_with_degrees.Cancer_Gene], x='NPCG', y='Degree', showfliers=False)\n",
    "plt.gca().set_xticklabels(['Other', 'NPCG'], fontsize=20)\n",
    "plt.xlabel(None)\n",
    "plt.gca().tick_params(axis='y', labelsize=18)\n",
    "plt.ylabel('Degree', fontsize=20)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=nodes_with_degrees, x='NPCG', y='KCG_interaction_fraction', showfliers=False)\n",
    "plt.ylabel('% of Interactions that\\nare with with KCGs', fontsize=20)\n",
    "plt.gca().set_xticklabels(['Other', 'NPCG'], fontsize=20)\n",
    "plt.gca().tick_params(axis='y', labelsize=18)\n",
    "plt.xlabel(None)\n",
    "plt.tight_layout()\n",
    "fig.savefig(os.path.join(reference_model_dir, 'npcgs_degree_kcginteractions.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 158)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nodes_with_degrees[nodes_with_degrees.NPCG].Cancer_Gene_Interactions >= 5).sum(), nodes_with_degrees.NPCG.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two-sided T-test P-value: 1.6482310030021656e-15\n"
     ]
    }
   ],
   "source": [
    "statistic, pval = scipy.stats.ttest_ind(nodes_with_degrees[nodes_with_degrees.NPCG].KCG_interaction_fraction,\n",
    "                                        nodes_with_degrees[~nodes_with_degrees.NPCG].KCG_interaction_fraction)\n",
    "print (\"Two-sided T-test P-value: {}\".format(pval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are NPCGs Interacting with KCGs Significantly more often?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-value: 4.56872773133489e-25\tOdds-Ratio: 5.615971311585088\n"
     ]
    }
   ],
   "source": [
    "kcg_interaction_threshold = 0.25\n",
    "\n",
    "high_kcg_int = nodes_with_degrees[nodes_with_degrees.KCG_interaction_fraction > kcg_interaction_threshold]\n",
    "high_kcg_novel = high_kcg_int.NPCG.sum()\n",
    "high_kcg_nonnovel = high_kcg_int[~high_kcg_int.NPCG].shape[0]\n",
    "low_kcg_novel = nodes_with_degrees[nodes_with_degrees.KCG_interaction_fraction <= kcg_interaction_threshold].NPCG.sum()\n",
    "low_kcg_nonnovel = (nodes_with_degrees[nodes_with_degrees.KCG_interaction_fraction <= kcg_interaction_threshold].shape[0] - low_kcg_novel)\n",
    "\n",
    "cont_table = [[high_kcg_novel, high_kcg_nonnovel], [low_kcg_novel, low_kcg_nonnovel]]\n",
    "\n",
    "# do fishers exact test\n",
    "odds_ratio, pvalue = scipy.stats.fisher_exact(cont_table)\n",
    "print (\"P-value: {}\\tOdds-Ratio: {}\".format(pvalue, odds_ratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 7))\n",
    "kwargs = dict(cumulative=True, linewidth=6, alpha=0.8)\n",
    "nodes_with_degrees.loc[nodes_with_degrees.KCG_interaction_fraction > 1, 'KCG_interaction_fraction'] = 1\n",
    "sns.distplot(nodes_with_degrees[nodes_with_degrees.NPCG].KCG_interaction_fraction,\n",
    "             label='NPCGs', kde_kws=kwargs, hist=False)#, hist_kws=kwargs, )\n",
    "sns.distplot(nodes_with_degrees[~nodes_with_degrees.NPCG].KCG_interaction_fraction,\n",
    "             label='Other', kde_kws=kwargs, hist=False) #hist_kws=kwargs, \n",
    "fig.savefig(os.path.join(all_models_dir, 'cum_dist_kcginteractions.svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Are NPCGs Essential in Tumor Cell Lines?\n",
    "The next step is to investigate if the candidate genes from our algorithm are essential in tumor cell lines from the Achilles data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74.88970708558337 2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load gene effect table and pre-process\n",
    "essential_genes = pd.read_csv(achilles_data_path).T\n",
    "essential_genes.columns = essential_genes.loc['Unnamed: 0']\n",
    "essential_genes.drop('Unnamed: 0', inplace=True)\n",
    "essential_genes['Name'] = [i.split('(')[0].strip() for i in essential_genes.index]\n",
    "essential_genes.set_index('Name', inplace=True)\n",
    "\n",
    "# compute number of affected cell lines for our newly predicted cancer genes\n",
    "def get_number_of_affected_cellines(gene_name, achilles_data):\n",
    "    # we only consider negative effects of knockouts because positives are often false\n",
    "    affected_celllines = (achilles_data[achilles_data.index == gene_name] < -0.5).sum().sum()\n",
    "    return affected_celllines\n",
    "\n",
    "target_scores = []\n",
    "for potential_target, _ in consensus_sorted:\n",
    "    num_affected_celllines = get_number_of_affected_cellines(potential_target,  essential_genes)\n",
    "    target_scores.append((potential_target, num_affected_celllines))\n",
    "\n",
    "# random expectation\n",
    "average = (essential_genes < -0.5).sum(axis=1).mean()\n",
    "median = (essential_genes < -0.5).sum(axis=1).median()\n",
    "print (average, median)\n",
    "\n",
    "# plot\n",
    "to_plot = target_scores[:30]\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "plt.gca().axvline(average, color='black', lw=5, alpha=.6, ls='--')\n",
    "#plt.gca().axvline(median, color='black', lw=5, alpha=.6, ls=':')\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h', color='darkorange')\n",
    "plt.xlabel('# of affected tumor cell lines', fontsize=12)\n",
    "plt.gca().tick_params(axis='both', labelsize=12)\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(all_models_dir, 'consensus_genes_achilles_affected_celllines.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "to_plot = [i for i in target_scores if i[1] > average][:20]\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "plt.gca().axvline(average, color='black', lw=5, alpha=.6, ls='--')\n",
    "#plt.gca().axvline(median, color='black', lw=5, alpha=.6, ls=':')\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h', color='darkorange')\n",
    "plt.xlabel('# of affected tumor cell lines', fontsize=12)\n",
    "plt.gca().tick_params(axis='both', labelsize=12)\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(all_models_dir, 'npcgs_essentiality_highconf.svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is this significant? Fishers exact test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[62, 2994], [101, 15176]]\n",
      "P-value: 4.851258666557485e-11\tOdds-Ratio: 3.1115432184501017\n"
     ]
    }
   ],
   "source": [
    "# Gene essentiality = more cell lines affected than average\n",
    "essentiality_scores = (essential_genes < -0.5).sum(axis=1) > average\n",
    "essentiality_scores.sum(), essentiality_scores.shape\n",
    "\n",
    "# formulate contingency table\n",
    "npcgs = [i[0] for i in consensus_sorted]\n",
    "essential_novel = essentiality_scores[essentiality_scores.index.isin(npcgs)].sum()\n",
    "essential_nonnovel = essentiality_scores[~essentiality_scores.index.isin(npcgs)].sum()\n",
    "nonessential_novel = np.logical_not(essentiality_scores[essentiality_scores.index.isin(npcgs)]).sum()\n",
    "nonessential_nonnovel = np.logical_not(essentiality_scores[~essentiality_scores.index.isin(npcgs)]).sum()\n",
    "cont_table = [[essential_novel, essential_nonnovel], [nonessential_novel, nonessential_nonnovel]]\n",
    "print (cont_table)\n",
    "\n",
    "# do fishers exact test\n",
    "odds_ratio, pvalue = scipy.stats.fisher_exact(cont_table)\n",
    "print (\"P-value: {}\\tOdds-Ratio: {}\".format(pvalue, odds_ratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "essentiality_numbers = (essential_genes < -0.5).sum(axis=1)\n",
    "pred_with_cellline_effect = pred_with_interactions.join(essentiality_numbers.rename('Essential_Cellines'),\n",
    "                                                        on='Name', how='inner'\n",
    "                                                       )\n",
    "npcgs_with_celline_effect = pred_with_cellline_effect[pred_with_cellline_effect.Name.isin(npcgs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>label</th>\n",
       "      <th>Num_Pos</th>\n",
       "      <th>Prob_pos</th>\n",
       "      <th>Std_Pred</th>\n",
       "      <th>NCG_Known_Cancer_Gene</th>\n",
       "      <th>NCG_Candidate_Cancer_Gene</th>\n",
       "      <th>OncoKB_Cancer_Gene</th>\n",
       "      <th>Bailey_et_al_Cancer_Gene</th>\n",
       "      <th>ONGene_Oncogene</th>\n",
       "      <th>Cancer_Gene_Interactions</th>\n",
       "      <th>Essential_Cellines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000150991</th>\n",
       "      <td>UBC</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>118.000</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000075624</th>\n",
       "      <td>ACTB</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>54.000</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000185591</th>\n",
       "      <td>SP1</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>67.000</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000096401</th>\n",
       "      <td>CDC5L</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>57.000</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000109971</th>\n",
       "      <td>HSPA8</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>80.000</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>HIST1H3I</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>78.000</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>HIST2H2BE</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>29.000</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000100603</th>\n",
       "      <td>SNW1</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>71.000</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000172531</th>\n",
       "      <td>PPP1CA</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>48.000</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000164924</th>\n",
       "      <td>YWHAZ</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>83.000</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000128731</th>\n",
       "      <td>HERC2</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>24.000</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000127481</th>\n",
       "      <td>UBR4</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6.000</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000060237</th>\n",
       "      <td>WNK1</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13.000</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000288121</th>\n",
       "      <td>MDN1</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7.000</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000072803</th>\n",
       "      <td>FBXW11</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>46.000</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000137076</th>\n",
       "      <td>TLN1</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20.000</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000272333</th>\n",
       "      <td>KMT2B</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>8.000</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000166851</th>\n",
       "      <td>PLK1</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>37.000</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000170312</th>\n",
       "      <td>CDK1</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>57.000</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000173039</th>\n",
       "      <td>RELA</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>70.000</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name  label  Num_Pos  Prob_pos  Std_Pred  \\\n",
       "ID                                                               \n",
       "ENSG00000150991        UBC  False       10     1.000     0.000   \n",
       "ENSG00000075624       ACTB  False       10     1.000     0.000   \n",
       "ENSG00000185591        SP1  False       10     1.000     0.000   \n",
       "ENSG00000096401      CDC5L  False       10     1.000     0.000   \n",
       "ENSG00000109971      HSPA8  False       10     1.000     0.000   \n",
       "NaN               HIST1H3I  False       10     1.000     0.000   \n",
       "NaN              HIST2H2BE  False       10     1.000     0.000   \n",
       "ENSG00000100603       SNW1  False       10     1.000     0.000   \n",
       "ENSG00000172531     PPP1CA  False       10     1.000     0.000   \n",
       "ENSG00000164924      YWHAZ  False       10     1.000     0.000   \n",
       "ENSG00000128731      HERC2  False       10     1.000     0.000   \n",
       "ENSG00000127481       UBR4  False       10     1.000     0.000   \n",
       "ENSG00000060237       WNK1  False       10     1.000     0.000   \n",
       "ENSG00000288121       MDN1  False       10     1.000     0.000   \n",
       "ENSG00000072803     FBXW11  False       10     1.000     0.000   \n",
       "ENSG00000137076       TLN1  False       10     1.000     0.000   \n",
       "ENSG00000272333      KMT2B  False       10     1.000     0.000   \n",
       "ENSG00000166851       PLK1  False       10     1.000     0.000   \n",
       "ENSG00000170312       CDK1  False       10     1.000     0.000   \n",
       "ENSG00000173039       RELA  False       10     1.000     0.000   \n",
       "\n",
       "                 NCG_Known_Cancer_Gene  NCG_Candidate_Cancer_Gene  \\\n",
       "ID                                                                  \n",
       "ENSG00000150991                  False                      False   \n",
       "ENSG00000075624                  False                       True   \n",
       "ENSG00000185591                  False                      False   \n",
       "ENSG00000096401                  False                      False   \n",
       "ENSG00000109971                  False                      False   \n",
       "NaN                              False                      False   \n",
       "NaN                              False                      False   \n",
       "ENSG00000100603                  False                      False   \n",
       "ENSG00000172531                  False                      False   \n",
       "ENSG00000164924                  False                      False   \n",
       "ENSG00000128731                  False                       True   \n",
       "ENSG00000127481                  False                      False   \n",
       "ENSG00000060237                  False                       True   \n",
       "ENSG00000288121                  False                      False   \n",
       "ENSG00000072803                  False                      False   \n",
       "ENSG00000137076                  False                      False   \n",
       "ENSG00000272333                  False                       True   \n",
       "ENSG00000166851                  False                      False   \n",
       "ENSG00000170312                  False                      False   \n",
       "ENSG00000173039                  False                       True   \n",
       "\n",
       "                 OncoKB_Cancer_Gene  Bailey_et_al_Cancer_Gene  \\\n",
       "ID                                                              \n",
       "ENSG00000150991               False                     False   \n",
       "ENSG00000075624               False                     False   \n",
       "ENSG00000185591               False                     False   \n",
       "ENSG00000096401               False                     False   \n",
       "ENSG00000109971               False                     False   \n",
       "NaN                            True                     False   \n",
       "NaN                           False                     False   \n",
       "ENSG00000100603               False                     False   \n",
       "ENSG00000172531               False                     False   \n",
       "ENSG00000164924               False                     False   \n",
       "ENSG00000128731               False                     False   \n",
       "ENSG00000127481               False                     False   \n",
       "ENSG00000060237               False                     False   \n",
       "ENSG00000288121               False                     False   \n",
       "ENSG00000072803               False                     False   \n",
       "ENSG00000137076               False                     False   \n",
       "ENSG00000272333                True                      True   \n",
       "ENSG00000166851               False                     False   \n",
       "ENSG00000170312               False                     False   \n",
       "ENSG00000173039               False                     False   \n",
       "\n",
       "                 ONGene_Oncogene  Cancer_Gene_Interactions  Essential_Cellines  \n",
       "ID                                                                              \n",
       "ENSG00000150991            False                   118.000                 612  \n",
       "ENSG00000075624            False                    54.000                 465  \n",
       "ENSG00000185591            False                    67.000                 130  \n",
       "ENSG00000096401             True                    57.000                 625  \n",
       "ENSG00000109971            False                    80.000                 187  \n",
       "NaN                        False                    78.000                 109  \n",
       "NaN                        False                    29.000                 260  \n",
       "ENSG00000100603            False                    71.000                 625  \n",
       "ENSG00000172531            False                    48.000                 460  \n",
       "ENSG00000164924             True                    83.000                 121  \n",
       "ENSG00000128731            False                    24.000                 135  \n",
       "ENSG00000127481            False                     6.000                 614  \n",
       "ENSG00000060237            False                    13.000                 611  \n",
       "ENSG00000288121            False                     7.000                 624  \n",
       "ENSG00000072803            False                    46.000                 244  \n",
       "ENSG00000137076            False                    20.000                 619  \n",
       "ENSG00000272333            False                     8.000                 111  \n",
       "ENSG00000166851             True                    37.000                 625  \n",
       "ENSG00000170312             True                    57.000                 625  \n",
       "ENSG00000173039            False                    70.000                 188  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npcgs_with_celline_effect[npcgs_with_celline_effect.Essential_Cellines > average].sort_values(by='Prob_pos', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(npcgs_with_celline_effect.Essential_Cellines >= 5).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/GCN/training/Rev1_CNA_separated_all_networks/CPDB_notsogood\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/.ipynb_checkpoints\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/CPDB\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/PCNet\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/STRINGdb\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/IRefIndex\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/Multinet\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/IRefIndex_2015\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/consensus_candidates_cnasep.tsv\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/consensus_genes_multiomics.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/ncgknown_neighbors_consensus.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/consensus_neighbors_all_networks.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/consensus_neighbors_all_networks_withrandom.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/correlation_cginteractions.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/consensus_genes_achilles_affected_celllines.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/npcgs_essentiality_highconf.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/known_housekeeping_score_distributions.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/achilles_known_housekeeping_genesets.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/achilles_housekeepingScore_genesets.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/achilles_housekeeping_correlation.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/achilles_effects_genesets.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/essentiality_genesets_grouped.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/achilles_effects_twothirds.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/achilles_effects_density.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/consensus_candidates_cnasep_keggformat.tsv\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/universe.tsv\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/consensus_candidates_cnasep_pathways.txt\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/aupr_heatmap.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/cum_dist_kcginteractions.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/aupr_heatmap.png\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/npcgs_degree_kcginteractions.pdf\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/npcgs_degree_kcginteractions.svg\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/npcgs_degree_kcginteractions.png\n",
      "../data/GCN/training/Rev1_CNA_separated_all_networks/auroc_heatmap.svg\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "No objects to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-8c355bee15f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mp_proc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mpred_processed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mprocess_pred\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_predictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_ppis\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mpredictions_all_networks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_processed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inner'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0mpredictions_all_networks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Avg_Pred'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredictions_all_networks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0mpred_with_cellline_effect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredictions_all_networks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0messentiality_numbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Essential_Cellines'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inner'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/pkg/python-3.7.7-0/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m         \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m         \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    282\u001b[0m     )\n\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/pkg/python-3.7.7-0/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 329\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No objects to concatenate\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "source": [
    "# get predictions for all networks\n",
    "checked_models = 0\n",
    "all_predictions = []\n",
    "all_ppis = []\n",
    "for network in os.listdir(all_models_dir):\n",
    "    network_path = os.path.join(all_models_dir, network)\n",
    "    predictions_network = None\n",
    "    if all_omics:\n",
    "        if os.path.isdir(network_path) and not network.startswith('.'):\n",
    "            for omics in os.listdir(network_path):\n",
    "                model_path = os.path.join(network_path, omics)\n",
    "                if os.path.isdir(model_path) and not omics.startswith('.'):\n",
    "                    predictions_network = postprocessing.load_predictions(model_path)\n",
    "    else:\n",
    "        print (network_path)\n",
    "        if os.path.isdir(network_path) and os.path.isdir(os.path.join(network_path, 'multiomics')):\n",
    "            model_path = os.path.join(network_path, 'multiomics')\n",
    "            predictions_network = postprocessing.load_predictions(model_path)\n",
    "    \n",
    "    if not predictions_network is None:\n",
    "        all_predictions.append(predictions_network)\n",
    "        all_ppis.append(network)\n",
    "\n",
    "# join together and add information on gene essentiality\n",
    "def process_pred(pred, network_name):\n",
    "    p_proc = pred.set_index('Name')\n",
    "    p_proc = p_proc.drop(['label', 'Num_Pos', 'Std_Pred'], axis=1)\n",
    "    p_proc.columns = ['Mean_{}'.format(network_name)]\n",
    "    return p_proc\n",
    "pred_processed = [process_pred(all_predictions[i], all_ppis[i]) for i in range(len(all_predictions))]\n",
    "predictions_all_networks = pd.concat(pred_processed, axis=1, join='inner')\n",
    "predictions_all_networks['Avg_Pred'] = predictions_all_networks.mean(axis=1)\n",
    "pred_with_cellline_effect = predictions_all_networks.join(essentiality_numbers.rename('Essential_Cellines'), how='inner')\n",
    "pred_all_npcgs = pred_with_cellline_effect[pred_with_cellline_effect.index.isin(npcgs)].sort_values(by='Avg_Pred', ascending=False)\n",
    "\n",
    "# plot the results sorted by the highest ranking genes across all networks\n",
    "# but only consider essential genes\n",
    "to_plot = pred_all_npcgs[pred_all_npcgs.Essential_Cellines > average].Essential_Cellines.head(20)\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "plt.gca().axvline(average, color='black', lw=5, alpha=.6, ls='--')\n",
    "#plt.gca().axvline(median, color='black', lw=5, alpha=.6, ls=':')\n",
    "sns.barplot(y=to_plot.index, x=to_plot.values, orient='h', color='darkorange')\n",
    "plt.xlabel('# of affected tumor cell lines', fontsize=12)\n",
    "plt.gca().tick_params(axis='both', labelsize=12)\n",
    "plt.ylabel(None)\n",
    "fig.tight_layout()\n",
    "#fig.savefig(os.path.join(all_models_dir, 'npcgs_essentiality_sortedallnetworks_overaverage.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'affected_celllines' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-49f6012101f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maffected_celllines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maffected_celllines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0maffected_celllines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0maffected_celllines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'affected_celllines' is not defined"
     ]
    }
   ],
   "source": [
    "affected_celllines[affected_celllines.sum(axis=1) > affected_celllines.sum(axis=1).mean()]\n",
    "affected_celllines.sum(axis=1).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fraction')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_name = 'GRB2'\n",
    "\n",
    "cellline_info = pd.read_csv('../data/pancancer/Achilles/sample_info.csv').set_index('DepMap_ID')\n",
    "cellline_info = cellline_info[cellline_info.index.isin(essential_genes.columns)]\n",
    "\n",
    "affected_celllines = (essential_genes[essential_genes.index == gene_name] < -0.5).T\n",
    "meta_for_gene = cellline_info[cellline_info.index.isin(affected_celllines.index[affected_celllines[gene_name] == True])]\n",
    "celllines_gene = pd.DataFrame(meta_for_gene.disease.value_counts())\n",
    "celllines_gene['Group'] = gene_name\n",
    "cellline_fractions = pd.DataFrame(cellline_info.disease.value_counts())\n",
    "cellline_fractions['Group'] = 'All Other Genes'\n",
    "to_plot = pd.concat((celllines_gene, cellline_fractions))\n",
    "to_plot['disease_label'] = to_plot.index\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "sns.barplot(data=to_plot, x='disease_label', y='disease', hue='Group')\n",
    "_ = plt.xticks(rotation=90, fontsize=15)\n",
    "plt.xlabel(None)\n",
    "plt.ylabel('Fraction', fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_with_cellline_effect = pred_with_cellline_effect.join(pd.DataFrame(adj, index=node_names[:, 1]).sum(axis=1).rename('Degree'), on='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disease</th>\n",
       "      <th>Group</th>\n",
       "      <th>disease_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Skin Cancer</th>\n",
       "      <td>17</td>\n",
       "      <td>GRB2</td>\n",
       "      <td>Skin Cancer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skin Cancer</th>\n",
       "      <td>42</td>\n",
       "      <td>All Other Genes</td>\n",
       "      <td>Skin Cancer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             disease            Group disease_label\n",
       "Skin Cancer       17             GRB2   Skin Cancer\n",
       "Skin Cancer       42  All Other Genes   Skin Cancer"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_plot[to_plot.index == 'Skin Cancer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "sns.kdeplot(pred_with_cellline_effect.Degree.rank(), pred_with_cellline_effect.Essential_Cellines.rank(), cmap='Reds',\n",
    "            shade=True, shade_lowest=False)\n",
    "rho, pval = scipy.stats.spearmanr(pred_with_cellline_effect.Degree, pred_with_cellline_effect.Essential_Cellines)\n",
    "plt.title('Corr. # affected celllines & Degree (R = {0:.2f}, pval = {1:.2f})'.format(rho, pval), fontsize=15)\n",
    "plt.xlabel('Degree (Ranked)', fontsize=20)\n",
    "plt.ylabel('# Celllines Affected (Ranked)', fontsize=20)\n",
    "fig.savefig(os.path.join(reference_model_dir, 'corr_celllines_degree.svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are NPCGs Housekeeping Genes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load gtex tissues\n",
    "dir_name = '../data/pancancer/TCGA/expression/tcga_expr_datadescriptor_normalized_batchcorr/'\n",
    "gtex_path = os.path.join(dir_name, '{}-rsem-fpkm-gtex.txt.gz')\n",
    "\n",
    "all_gtex_tissues = []\n",
    "tissue_pairs = [('bladder', 'blca'), ('breast', 'brca'), ('cervix', 'cesc'),\n",
    "                ('uterus', 'ucec'), ('colon', 'read'),\n",
    "                ('colon', 'coad'), ('liver', 'lihc'), ('salivary', 'hnsc'),\n",
    "                ('esophagus_mus', 'esca'), ('prostate', 'prad'), ('stomach', 'stad'),\n",
    "                ('thyroid', 'thca'), ('lung', 'luad'), ('lung', 'lusc'),\n",
    "                ('kidney', 'kirc'), ('kidney', 'kirp')]\n",
    "for gtex_tissue, tcga_project in tissue_pairs:\n",
    "    ge_tissue = pd.read_csv(gtex_path.format(gtex_tissue), compression='gzip', sep='\\t')\n",
    "    ge_tissue = ge_tissue.set_index('Hugo_Symbol').drop('Entrez_Gene_Id', axis=1)\n",
    "    all_gtex_tissues.append(ge_tissue)\n",
    "\n",
    "# concatenate to obtain one single gene x sample matrix\n",
    "all_gtex_samples = pd.concat(all_gtex_tissues, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAAS</td>\n",
       "      <td>NM_015665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAGAB</td>\n",
       "      <td>NM_024666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAMP</td>\n",
       "      <td>NM_001087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAR2</td>\n",
       "      <td>NM_015511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AARS</td>\n",
       "      <td>NM_001605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol         ID\n",
       "0   AAAS  NM_015665\n",
       "1  AAGAB  NM_024666\n",
       "2   AAMP  NM_001087\n",
       "3   AAR2  NM_015511\n",
       "4   AARS  NM_001605"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load some known housekeeping genes for quality control\n",
    "known_hk_genes = pd.read_csv('../data/HK_genes.txt', sep='\\t', header=None, names=['Symbol', 'ID'])\n",
    "known_hk_genes['Symbol'] = known_hk_genes.Symbol.str.strip()\n",
    "known_hk_genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20089 genes considered\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute median and std for gene expression\n",
    "ge_median = all_gtex_samples.median(axis=1)\n",
    "ge_var = all_gtex_samples.std(axis=1)\n",
    "\n",
    "# compute a score that tells us how \"housekeeping\" a gene is\n",
    "score = ge_median * (1. / ge_var)\n",
    "#score = all_gtex_samples[ge_median > 150].std(axis=1)\n",
    "print (\"{} genes considered\".format(score.shape[0]))\n",
    "gene_scores = pd.DataFrame(score.sort_values(), columns=['Variance'])\n",
    "gene_scores['Rank'] = np.arange(gene_scores.shape[0]) + 1\n",
    "\n",
    "# verify that the housekeeping genes have a higher rank\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "sns.distplot(gene_scores[gene_scores.index.isin(known_hk_genes.Symbol)].Rank, label='Housekeeping Genes')\n",
    "sns.distplot(gene_scores.Rank, label='All Genes')\n",
    "sns.distplot(gene_scores[gene_scores.index.isin(known_cancer_genes)].Rank, label='Cancer Genes')\n",
    "plt.ylabel('Density', fontsize=16)\n",
    "plt.xlabel('Ranked Scores', fontsize=16)\n",
    "plt.legend(fontsize=18)\n",
    "fig.savefig(os.path.join(all_models_dir, 'known_housekeeping_score_distributions.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DF for essentiality\n",
    "gene_effects = (essential_genes < -0.5).sum(axis=1).rename('Celllines_Affected')\n",
    "consensus_genes = [i[0] for i in consensus_sorted]\n",
    "gene_effects_copy = pd.DataFrame(gene_effects.copy())\n",
    "gene_effects_copy['Gene_Set'] = 'Other'\n",
    "gene_effects_copy.loc[gene_effects_copy.index.isin(consensus_genes), 'Gene_Set'] = 'Novel\\nPredictions'\n",
    "gene_effects_copy.loc[gene_effects_copy.index.isin(known_cancer_genes), 'Gene_Set'] = 'Known Cancer\\nGenes\\n(NCG)'\n",
    "gene_effects_copy.loc[gene_effects_copy.index.isin(candidate_cancer_genes), 'Gene_Set'] = 'Candidate\\nGenes\\n(NCG)'\n",
    "\n",
    "# add housekeeping information\n",
    "gene_effects_housekeeping = gene_effects_copy.join(score.rename('HousekeepingScore')).dropna()\n",
    "gene_effects_housekeeping['IsHousekeeping'] = False\n",
    "gene_effects_housekeeping.loc[gene_effects_housekeeping.index.isin(known_hk_genes.Symbol), 'IsHousekeeping'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 5))\n",
    "sns.barplot(data=gene_effects_housekeeping, x='Gene_Set', y='IsHousekeeping')\n",
    "plt.xlabel(None)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "plt.ylabel('Fraction of Known\\nHousekeeping Genes', fontsize=18)\n",
    "plt.gca().tick_params(axis='both', labelsize=14)\n",
    "fig.savefig(os.path.join(all_models_dir, 'achilles_known_housekeeping_genesets.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 5))\n",
    "sns.boxplot(data=gene_effects_housekeeping, x='Gene_Set', y='HousekeepingScore', showfliers=False)\n",
    "#sns.swarmplot(data=gene_effects_copy, x='Gene_Set', y='Celllines_Affected')\n",
    "plt.xlabel(None)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "#plt.ylabel('# of Affected Cell Lines', fontsize=18)\n",
    "plt.ylabel('Housekeeping Score ($\\mu \\\\times \\\\frac{1}{\\sqrt{\\sigma}}$)', fontsize=18)\n",
    "plt.gca().tick_params(axis='both', labelsize=14)\n",
    "fig.savefig(os.path.join(all_models_dir, 'achilles_housekeepingScore_genesets.svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are Essential Genes Enriched for Housekeeping Function?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson Correlation: 0.38699513845506367\tP-value: 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation, pvalue = scipy.stats.spearmanr(gene_effects_housekeeping.Celllines_Affected,\n",
    "                                           gene_effects_housekeeping.HousekeepingScore\n",
    "                                          )\n",
    "sns.kdeplot(gene_effects_housekeeping.Celllines_Affected.rank(),\n",
    "            gene_effects_housekeeping.HousekeepingScore.rank(),\n",
    "            cmap='Reds',\n",
    "            shade=True, shade_lowest=False\n",
    "           )\n",
    "plt.ylabel('Housekeeping Score ($\\mu \\\\times \\\\frac{1}{\\sqrt{\\sigma}}$)', fontsize=16)\n",
    "plt.xlabel('# of Affected Cell lines', fontsize=16)\n",
    "print (\"Pearson Correlation: {}\\tP-value: {}\".format(correlation, pvalue))\n",
    "fig.savefig(os.path.join(reference_model_dir, 'achilles_housekeeping_correlation.svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Essentiality Across Gene Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 5))\n",
    "sns.boxplot(data=gene_effects_copy, x='Gene_Set', y='Celllines_Affected', showfliers=False)\n",
    "#sns.swarmplot(data=gene_effects_copy, x='Gene_Set', y='Celllines_Affected')\n",
    "plt.xlabel(None)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "plt.ylabel('# of Affected Cell Lines', fontsize=18)\n",
    "plt.gca().tick_params(axis='both', labelsize=14)\n",
    "fig.savefig(os.path.join(all_models_dir, 'achilles_effects_genesets.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.015304263373330615\n"
     ]
    }
   ],
   "source": [
    "# significance test between NPCGs and KCGs\n",
    "kcgs = gene_effects_copy[gene_effects_copy.Gene_Set == 'Known Cancer\\nGenes\\n(NCG)']\n",
    "npcgs = gene_effects_copy[gene_effects_copy.Gene_Set == 'Novel\\nPredictions']\n",
    "npcgs_essential = (npcgs.Celllines_Affected > 78).sum()\n",
    "npcgs_nonessential = npcgs.shape[0] - npcgs_essential\n",
    "kcgs_essential = (kcgs.Celllines_Affected > 78).sum()\n",
    "kcgs_nonessential = kcgs.shape[0] - kcgs_essential\n",
    "\n",
    "cont = [[npcgs_essential, npcgs_nonessential], [kcgs_essential, kcgs_nonessential]]\n",
    "odds_ratio, pvalue = scipy.stats.fisher_exact(cont)\n",
    "print (pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked_barplot_vals = {}\n",
    "gene_sets = gene_effects_copy.Gene_Set.unique()\n",
    "groups = list(range(len(gene_sets)))\n",
    "fractions = [(0, 625./10.), (625./10., 625./2.), (625./2., 625)]\n",
    "\n",
    "for gene_set in gene_sets:\n",
    "    frac_vals = []\n",
    "    for min_v, max_v in fractions:\n",
    "        v = gene_effects_copy[gene_effects_copy.Gene_Set == gene_set].Celllines_Affected.between(min_v, max_v).sum()\n",
    "        frac_vals.append(v)\n",
    "    stacked_barplot_vals[gene_set] = frac_vals\n",
    "\n",
    "df = pd.DataFrame(stacked_barplot_vals, index=['0_10', '10_50', '50_100']).T\n",
    "totals = [i+j+k for i,j,k in zip(df['0_10'], df['10_50'], df['50_100'])]\n",
    "bars_0_10 = [i / j * 100 for i,j in zip(df['0_10'], totals)]\n",
    "bars_10_50 = [i / j * 100 for i,j in zip(df['10_50'], totals)]\n",
    "bars_50_100 = [i / j * 100 for i,j in zip(df['50_100'], totals)]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "plt.bar(groups, bars_0_10, color='gray', label='$0 \\leq x \\leq 10$%')\n",
    "plt.bar(groups, bars_10_50, bottom=bars_0_10, color='green', label='$10 \\leq x \\leq 50$%')\n",
    "plt.bar(groups, bars_50_100, bottom=[i+j for i,j in zip(bars_0_10, bars_10_50)],\n",
    "        color='darkred', label='$50 \\leq x \\leq 100$%')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.01, 1.05), ncol=1, fontsize=20)\n",
    "plt.ylabel('Percent Essentiality', fontsize=20)\n",
    "_ = plt.xticks(groups, gene_sets, fontsize=15)\n",
    "plt.gca().tick_params(axis='both', which='major', labelsize=20)\n",
    "fig.savefig(os.path.join(all_models_dir, 'essentiality_genesets_grouped.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 5))\n",
    "gene_effects_copy['onethird'] = gene_effects_copy.Celllines_Affected.between(0, 417)\n",
    "#essential_thr = gene_effects_copy.Celllines_Affected.mean()\n",
    "essential_thr = 78\n",
    "gene_effects_essentialonly = gene_effects_copy[gene_effects_copy.Celllines_Affected > essential_thr]\n",
    "sns.barplot(data=gene_effects_essentialonly, x='Gene_Set', y='onethird')\n",
    "plt.xlabel(None)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "plt.ylabel('Fraction of Essential Genes with Effect \\n in $\\leq \\\\frac{2}{3}$ of Celllines', fontsize=18)\n",
    "#plt.ylabel('Fraction of Genes with Effect \\n in $\\\\frac{1}{3} \\leq x \\leq \\\\frac{2}{3}$ of Celllines', fontsize=18)\n",
    "plt.gca().tick_params(axis='both', labelsize=14)\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(all_models_dir, 'achilles_effects_twothirds.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 5))\n",
    "gene_effects_copy['onethird'] = gene_effects_copy.Celllines_Affected.between(208, 417)\n",
    "#essential_thr = gene_effects_copy.Celllines_Affected.mean()\n",
    "essential_thr = 78\n",
    "gene_effects_essentialonly = gene_effects_copy[gene_effects_copy.Celllines_Affected > essential_thr]\n",
    "#sns.barplot(data=gene_effects_essentialonly, x='Gene_Set', y='onethird')\n",
    "for i in gene_effects_essentialonly.Gene_Set.unique():\n",
    "    d = gene_effects_copy[gene_effects_copy.Gene_Set == i]\n",
    "    sns.kdeplot(d.Celllines_Affected, label=i)\n",
    "    #sns.distplot(d.Celllines_Affected, kde=False, label=i, bins=np.linspace(78, 625, 100), norm_hist=True)\n",
    "plt.legend()\n",
    "#sns.kdeplot(data=gene_effects_essentialonly, x='onethird', hue='Gene_Set')\n",
    "plt.xlabel(None)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_visible(False)\n",
    "plt.ylabel('Density of # of Affected Celllines', fontsize=18)\n",
    "#plt.ylabel('Fraction of Essential Genes with Effect \\n in $\\\\frac{1}{3} \\leq x \\leq \\\\frac{2}{3}$ of Celllines', fontsize=18)\n",
    "#plt.ylabel('Fraction of Genes with Effect \\n in $\\\\frac{1}{3} \\leq x \\leq \\\\frac{2}{3}$ of Celllines', fontsize=18)\n",
    "plt.gca().tick_params(axis='both', labelsize=14)\n",
    "#plt.xlim([0, 700])\n",
    "fig.tight_layout()\n",
    "fig.savefig(os.path.join(all_models_dir, 'achilles_effects_density.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>label</th>\n",
       "      <th>Num_Pos</th>\n",
       "      <th>Prob_pos</th>\n",
       "      <th>Std_Pred</th>\n",
       "      <th>NCG_Known_Cancer_Gene</th>\n",
       "      <th>NCG_Candidate_Cancer_Gene</th>\n",
       "      <th>OncoKB_Cancer_Gene</th>\n",
       "      <th>Bailey_et_al_Cancer_Gene</th>\n",
       "      <th>ONGene_Oncogene</th>\n",
       "      <th>Cancer_Gene_Interactions</th>\n",
       "      <th>Essential_Cellines</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000100393</th>\n",
       "      <td>EP300</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>121.000</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000133703</th>\n",
       "      <td>KRAS</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>24.000</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000155657</th>\n",
       "      <td>TTN</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>18.000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000284792</th>\n",
       "      <td>PTEN</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>32.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000039068</th>\n",
       "      <td>CDH1</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>66.000</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  label  Num_Pos  Prob_pos  Std_Pred  \\\n",
       "ID                                                           \n",
       "ENSG00000100393  EP300   True       10     1.000     0.000   \n",
       "ENSG00000133703   KRAS   True       10     1.000     0.000   \n",
       "ENSG00000155657    TTN  False       10     1.000     0.000   \n",
       "ENSG00000284792   PTEN   True       10     1.000     0.000   \n",
       "ENSG00000039068   CDH1   True       10     1.000     0.000   \n",
       "\n",
       "                 NCG_Known_Cancer_Gene  NCG_Candidate_Cancer_Gene  \\\n",
       "ID                                                                  \n",
       "ENSG00000100393                   True                      False   \n",
       "ENSG00000133703                   True                      False   \n",
       "ENSG00000155657                  False                       True   \n",
       "ENSG00000284792                   True                      False   \n",
       "ENSG00000039068                   True                      False   \n",
       "\n",
       "                 OncoKB_Cancer_Gene  Bailey_et_al_Cancer_Gene  \\\n",
       "ID                                                              \n",
       "ENSG00000100393                True                      True   \n",
       "ENSG00000133703                True                      True   \n",
       "ENSG00000155657               False                     False   \n",
       "ENSG00000284792                True                      True   \n",
       "ENSG00000039068                True                      True   \n",
       "\n",
       "                 ONGene_Oncogene  Cancer_Gene_Interactions  Essential_Cellines  \n",
       "ID                                                                              \n",
       "ENSG00000100393            False                   121.000                 266  \n",
       "ENSG00000133703             True                    24.000                 247  \n",
       "ENSG00000155657            False                    18.000                   8  \n",
       "ENSG00000284792            False                    32.000                   2  \n",
       "ENSG00000039068             True                    66.000                  34  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_with_cellline_effect.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/pkg/python-3.7.7-0/lib/python3.7/site-packages/pandas/core/generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "pred_with_effects = pred_with_cellline_effect[['Prob_pos', 'Essential_Cellines']]\n",
    "pred_with_effects.columns = ['Prob_pos', 'Celllines_Affected']\n",
    "pred_with_effects['Effect_Rank'] = pred_with_effects.Celllines_Affected.rank()\n",
    "pred_with_effects.Effect_Rank.fillna(0, inplace=True)\n",
    "sns.kdeplot(pred_with_effects.Prob_pos.rank(), pred_with_effects.Effect_Rank, cmap='Reds',\n",
    "            shade=True, shade_lowest=False)\n",
    "rho, pval = scipy.stats.spearmanr(pred_with_effects.Prob_pos, pred_with_effects.Celllines_Affected)\n",
    "plt.title('Correlation between # affected celllines & Output Probability (R = {0:.2f}, pval = R = {1:.2f})'.format(rho, pval), fontsize=15)\n",
    "plt.xlabel('EMOGI Score (Ranked)', fontsize=20)\n",
    "plt.ylabel('# Celllines Affected (Ranked)', fontsize=20)\n",
    "fig.savefig(os.path.join(reference_model_dir, 'corr_celllines_output.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'candidate_cancer_genes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-4fa72e839bb1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# add column for presence in NCG Candidate Cancer Genes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mconsensus_cancergenes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'NCG_Candidates'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconsensus_cancergenes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcandidate_cancer_genes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# add column for presence in OncoKB (anywhere there, not only high confidence)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'candidate_cancer_genes' is not defined"
     ]
    }
   ],
   "source": [
    "consensus_cancergenes = pd.DataFrame(consensus_sorted, columns=['Name', 'Models_top{}'.format(top_n)]).set_index('Name')\n",
    "\n",
    "# add column for presence in NCG Candidate Cancer Genes\n",
    "consensus_cancergenes['NCG_Candidates'] = consensus_cancergenes.index.isin(candidate_cancer_genes)\n",
    "\n",
    "# add column for presence in OncoKB (anywhere there, not only high confidence)\n",
    "oncokb_genes = pd.read_csv('/project/gcn/diseasegcn/data/pancancer/oncoKB/cancerGeneList.txt', sep='\\t')\n",
    "consensus_cancergenes['OncoKB'] = consensus_cancergenes.index.isin(oncokb_genes['Hugo Symbol'])\n",
    "\n",
    "# add column for the number of cell lines in which the gene is essential (Achilles data)\n",
    "consensus_effects = pd.DataFrame(target_scores, columns=['Name', 'Affected_Celllines']).set_index('Name')\n",
    "consensus_cancergenes = consensus_cancergenes.join(consensus_effects)\n",
    "#consensus_cancergenes.to_csv(os.path.join(all_models_dir, 'consensus_candidates_cnasep.tsv'), sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if NPCGs are Similar When CNAs Are Separate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 212, 165)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_npcgs = pd.read_csv('../data/GCN/training/final_TCGA_all_networks/consensus_candidates.tsv', sep='\\t')\n",
    "len(set(old_npcgs.Name).intersection(set(npcgs))), len(old_npcgs), len(consensus_sorted)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
